A nonparametric method of compiling LIFE TABLES or survival tables. It combines calculated probabilities of survival and estimates to allow for observations occurring beyond a measurement threshold, which are assumed to occur randomly. Time intervals are defined as ending each time an event occurs and are therefore unequal. (From Last, A Dictionary of Epidemiology, 1995)
Studies used to test etiologic hypotheses in which inferences about an exposure to putative causal factors are derived from data relating to characteristics of persons under study or to events or experiences in their past. The essential feature is that some of the persons under study have the disease or outcome of interest and their characteristics are compared with those of unaffected persons.
Statistical models used in survival analysis that assert that the effect of the study factors on the hazard rate in the study population is multiplicative and does not change over time.
A prediction of the probable outcome of a disease based on a individual's condition and the usual course of the disease as seen in similar situations.
Evaluation undertaken to assess the results or consequences of management and procedures used in combating disease in order to determine the efficacy, effectiveness, safety, and practicability of these interventions in individual cases or series.
The proportion of survivors in a group, e.g., of patients, studied and followed over a period, or the proportion of persons in a specified group alive at the beginning of a time interval who survive to the end of the interval. It is often studied using life table methods.
Elements of limited time intervals, contributing to particular results or situations.
Studies in which individuals or populations are followed to assess the outcome of exposures, procedures, or effects of a characteristic, e.g., occurrence of disease.
A strain of Murine leukemia virus (LEUKEMIA VIRUS, MURINE) isolated from radiation-induced lymphomas in C57BL mice. It is leukemogenic, thymotrophic, can be transmitted vertically, and replicates only in vivo.
Period after successful treatment in which there is no appearance of the symptoms or effects of the disease.
A class of statistical procedures for estimating the survival function (function of time, starting with a population 100% well at a given time and providing the percentage of the population still well at later times). The survival analysis is then used for making inferences about the effects of treatments, prognostic factors, exposures, and other covariates on the function.
Studies in which subsets of a defined population are identified. These groups may or may not be exposed to factors hypothesized to influence the probability of the occurrence of a particular disease or other outcome. Cohorts are defined populations which, as a whole, are followed in an attempt to determine distinguishing subgroup characteristics.
Compounds used extensively as acetylation, oxidation and dehydrating agents and in the modification of proteins and enzymes.
An aspect of personal behavior or lifestyle, environmental exposure, or inborn or inherited characteristic, which, on the basis of epidemiologic evidence, is known to be associated with a health-related condition considered important to prevent.
Spherical phototrophic bacteria found in mud and stagnant water exposed to light.
Methods which attempt to express in replicable terms the extent of the neoplasm in the patient.
A species of VARICELLOVIRUS producing a respiratory infection (PSEUDORABIES) in swine, its natural host. It also produces an usually fatal ENCEPHALOMYELITIS in cattle, sheep, dogs, cats, foxes, and mink.
Observation of a population for a sufficient number of persons over a sufficient number of years to generate incidence or mortality rates subsequent to the selection of the study group.
Descriptions of specific amino acid, carbohydrate, or nucleotide sequences which have appeared in the published literature and/or are deposited in and maintained by databanks such as GENBANK, European Molecular Biology Laboratory (EMBL), National Biomedical Research Foundation (NBRF), or other sequence repositories.
Statistical formulations or analyses which, when applied to data and found to fit the data, are then used to verify the assumptions and parameters used in the analysis. Examples of statistical models are the linear model, binomial model, polynomial model, two-parameter model, etc.
The statistical reproducibility of measurements (often in a clinical context), including the testing of instrumentation or techniques to obtain reproducible results. The concept includes reproducibility of physiological measurements, which may be used to develop rules to assess probability or prognosis, or response to a stimulus; reproducibility of occurrence of a condition; and reproducibility of experimental results.
A procedure consisting of a sequence of algebraic formulas and/or logical steps to calculate or determine a given task.
The term "United States" in a medical context often refers to the country where a patient or study participant resides, and is not a medical term per se, but relevant for epidemiological studies, healthcare policies, and understanding differences in disease prevalence, treatment patterns, and health outcomes across various geographic locations.
Computer-based representation of physical systems and phenomena such as chemical processes.
The total number of cases of a given disease in a specified population at a designated time. It is differentiated from INCIDENCE, which refers to the number of new cases in the population at a given time.
Theoretical representations that simulate the behavior or activity of genetic processes or phenomena. They include the use of mathematical equations, computers, and other electronic equipment.
Functions constructed from a statistical model and a set of observed data which give the probability of that data for various values of the unknown model parameters. Those parameter values that maximize the probability are the maximum likelihood estimates of the parameters.
A theorem in probability theory named for Thomas Bayes (1702-1761). In epidemiology, it is used to obtain the probability of disease in a group of people with some characteristic on the basis of the overall rate of that disease and of the likelihood of that characteristic in healthy and diseased individuals. The most familiar application is in clinical decision analysis where it is used for estimating the probability of a particular diagnosis given the appearance of some symptoms or test result.
Theoretical representations that simulate the behavior or activity of biological processes or diseases. For disease models in living animals, DISEASE MODELS, ANIMAL is available. Biological models include the use of mathematical equations, computers, and other electronic equipment.
Theoretical representations that simulate the behavior or activity of systems, processes, or phenomena. They include the use of mathematical equations, computers, and other electronic equipment.
Application of statistical procedures to analyze specific observed or assumed facts from a particular study.
The number of new cases of a given disease during a given period in a specified population. It also is used for the rate at which new events occur in a defined population. It is differentiated from PREVALENCE, which refers to all cases, new or old, in the population at a given time.
Procedures for finding the mathematical function which best describes the relationship between a dependent variable and one or more independent variables. In linear regression (see LINEAR MODELS) the relationship is constrained to be a straight line and LEAST-SQUARES ANALYSIS is used to determine the best fit. In logistic regression (see LOGISTIC MODELS) the dependent variable is qualitative rather than continuously variable and LIKELIHOOD FUNCTIONS are used to find the best relationship. In multiple regression, the dependent variable is considered to depend on more than a single independent variable.
The qualitative or quantitative estimation of the likelihood of adverse effects that may result from exposure to specified health hazards or from the absence of beneficial influences. (Last, Dictionary of Epidemiology, 1988)
The production of offspring by selective mating or HYBRIDIZATION, GENETIC in animals or plants.
Genotypic differences observed among individuals in a population.
In statistics, a technique for numerically approximating the solution of a mathematical problem by studying the distribution of some random variable, often generated by a computer. The name alludes to the randomness characteristic of the games of chance played at the gambling casinos in Monte Carlo. (From Random House Unabridged Dictionary, 2d ed, 1993)
Age as a constituent element or influence contributing to the production of a result. It may be applicable to the cause or the effect of a circumstance. It is used with human or animal concepts but should be differentiated from AGING, a physiological process, and TIME FACTORS which refers only to the passage of time.
Binary classification measures to assess test results. Sensitivity or recall rate is the proportion of true positives. Specificity is the probability of correctly determining the absence of a condition. (From Last, Dictionary of Epidemiology, 2d ed)
The study of chance processes or the relative frequency characterizing a chance process.
Statistical models in which the value of a parameter for a given value of a factor is assumed to be equal to a + bx, where a and b are constants. The models predict a linear regression.
An infant during the first month after birth.
Ongoing scrutiny of a population (general population, study population, target population, etc.), generally using methods distinguished by their practicability, uniformity, and frequently their rapidity, rather than by complete accuracy.
A method of comparing the cost of a program with its expected benefits in dollars (or other currency). The benefit-to-cost ratio is a measure of total return expected per unit of money spent. This analysis generally excludes consideration of factors that are not measured ultimately in economic terms. Cost effectiveness compares alternative ways to achieve a specific set of results.
The frequency of different ages or age groups in a given population. The distribution may refer to either how many or what proportion of the group. The population is usually patients with a specific disease but the concept is not restricted to humans and is not restricted to medicine.
The status during which female mammals carry their developing young (EMBRYOS or FETUSES) in utero before birth, beginning from FERTILIZATION to BIRTH.
Studies in which the presence or absence of disease or other health-related variables are determined in each member of the study population or in a representative sample at one particular time. This contrasts with LONGITUDINAL STUDIES which are followed over a period of time.
Research techniques that focus on study designs and data gathering methods in human and animal populations.
The discipline studying genetic composition of populations and effects of factors such as GENETIC SELECTION, population size, MUTATION, migration, and GENETIC DRIFT on the frequencies of various GENOTYPES and PHENOTYPES using a variety of GENETIC TECHNIQUES.
The relationships of groups of organisms as reflected by their genetic makeup.
A systematic collection of factual data pertaining to health and disease in a human population within a given geographic area.
Statistical models which describe the relationship between a qualitative dependent variable (that is, one which can take only certain discrete values, such as the presence or absence of a disease) and an independent variable. A common application is in epidemiology for estimating an individual's risk (probability of a disease) as a function of a given risk factor.
Studies which start with the identification of persons with a disease of interest and a control (comparison, referent) group without the disease. The relationship of an attribute to the disease is examined by comparing diseased and non-diseased persons with regard to the frequency or levels of the attribute in each group.
The probability that an event will occur. It encompasses a variety of measures of the probability of a generally unfavorable outcome.
Systematic gathering of data for a particular purpose from various sources, including questionnaires, interviews, observation, existing records, and electronic devices. The process is usually preliminary to statistical analysis of the data.
Predetermined sets of questions used to collect data - clinical data, social status, occupational group, etc. The term is often applied to a self-completed survey instrument.
The actual costs of providing services related to the delivery of health care, including the costs of procedures, therapies, and medications. It is differentiated from HEALTH EXPENDITURES, which refers to the amount of money paid for the services, and from fees, which refers to the amount charged, regardless of cost.
The monitoring of the level of toxins, chemical pollutants, microbial contaminants, or other harmful substances in the environment (soil, air, and water), workplace, or in the bodies of people and animals present in that environment.
The personal cost of acute or chronic disease. The cost to the patient may be an economic, social, or psychological cost or personal loss to self, family, or immediate community. The cost of illness may be reflected in absenteeism, productivity, response to treatment, peace of mind, or QUALITY OF LIFE. It differs from HEALTH CARE COSTS, meaning the societal cost of providing services related to the delivery of health care, rather than personal impact on individuals.
A range of values for a variable of interest, e.g., a rate, constructed so that this range has a specified probability of including the true value of the variable.
The science and art of collecting, summarizing, and analyzing data that are subject to random variation. The term is also applied to the data themselves and to the summarization of the data.
Maleness or femaleness as a constituent element or influence contributing to the production of a result. It may be applicable to the cause or effect of a circumstance. It is used with human or animal concepts but should be differentiated from SEX CHARACTERISTICS, anatomical or physiological manifestations of sex, and from SEX DISTRIBUTION, the number of males and females in given circumstances.
A stochastic process such that the conditional probability distribution for a state at any future instant, given the present state, is unaffected by any additional knowledge of the past history of the system.
The process of cumulative change at the level of DNA; RNA; and PROTEINS, over successive generations.
The number of units (persons, animals, patients, specified circumstances, etc.) in a population to be studied. The sample size should be big enough to have a high likelihood of detecting a true difference between two groups. (From Wassertheil-Smoller, Biostatistics and Epidemiology, 1990, p95)
The science dealing with the earth and its life, especially the description of land, sea, and air and the distribution of plant and animal life, including humanity and human industries with reference to the mutual relations of these elements. (From Webster, 3d ed)
A measurement index derived from a modification of standard life-table procedures and designed to take account of the quality as well as the duration of survival. This index can be used in assessing the outcome of health care procedures or services. (BIOETHICS Thesaurus, 1994)
The ratio of two odds. The exposure-odds ratio for case control data is the ratio of the odds in favor of exposure among cases to the odds in favor of exposure among noncases. The disease-odds ratio for a cohort or cross section is the ratio of the odds in favor of disease among the exposed to the odds in favor of disease among the unexposed. The prevalence-odds ratio refers to an odds ratio derived cross-sectionally from studies of prevalent cases.
The exposure to potentially harmful chemical, physical, or biological agents in the environment or to environmental factors that may include ionizing radiation, pathogenic organisms, or toxic chemicals.
The exposure to potentially harmful chemical, physical, or biological agents that occurs as a result of one's occupation.
Number of individuals in a population relative to space.
A characteristic showing quantitative inheritance such as SKIN PIGMENTATION in humans. (From A Dictionary of Genetics, 4th ed)
The condition in which reasonable knowledge regarding risks, benefits, or the future is not available.
A plan for collecting and utilizing data so that desired information can be obtained with sufficient precision or so that an hypothesis can be tested properly.
The mass or quantity of heaviness of an individual. It is expressed by units of pounds or kilograms.
Absolute, comparative, or differential costs pertaining to services, institutions, resources, etc., or the analysis and study of these costs.
The deductive study of shape, quantity, and dependence. (From McGraw-Hill Dictionary of Scientific and Technical Terms, 6th ed)
Divisions of the year according to some regularly recurrent phenomena usually astronomical or climatic. (From McGraw-Hill Dictionary of Scientific and Technical Terms, 6th ed)
The number of males and females in a given population. The distribution may refer to how many men or women or what proportion of either in the group. The population is usually patients with a specific disease but the concept is not restricted to humans and is not restricted to medicine.
Domesticated bovine animals of the genus Bos, usually kept on a farm or ranch and used for the production of meat or dairy products or for heavy labor.
A principle of estimation in which the estimates of a set of parameters in a statistical model are those quantities minimizing the sum of squared differences between the observed values of a dependent variable and the values predicted by the model.
Statistical models of the production, distribution, and consumption of goods and services, as well as of financial considerations. For the application of statistics to the testing and quantifying of economic theories MODELS, ECONOMETRIC is available.
The genetic constitution of the individual, comprising the ALLELES present at each GENETIC LOCUS.
In screening and diagnostic tests, the probability that a person with a positive test is a true positive (i.e., has the disease), is referred to as the predictive value of a positive test; whereas, the predictive value of a negative test is the probability that the person with a negative test does not have the disease. Predictive value is related to the sensitivity and specificity of the test.
Sequential operating programs and data which instruct the functioning of a digital computer.
The application of mathematical formulas and statistical techniques to the testing and quantifying of economic theories and the solution of economic problems.
A statistical technique that isolates and assesses the contributions of categorical independent variables to variation in the mean of a continuous dependent variable.
A distribution function used to describe the occurrence of rare events or to describe the sampling distribution of isolated counts in a continuum of time or space.
The amount of radiation energy that is deposited in a unit mass of material, such as tissues of plants or animal. In RADIOTHERAPY, radiation dosage is expressed in gray units (Gy). In RADIOLOGIC HEALTH, the dosage is expressed by the product of absorbed dose (Gy) and quality factor (a function of linear energy transfer), and is called radiation dose equivalent in sievert units (Sv).
Differential and non-random reproduction of different genotypes, operating to alter the gene frequencies within a population.
The use of statistical and mathematical methods to analyze biological observations and phenomena.
Extensive collections, reputedly complete, of facts and data garnered from material of a specialized subject area and made available for analysis and application. The collection can be automated by various contemporary methods for retrieval. The concept should be differentiated from DATABASES, BIBLIOGRAPHIC which is restricted to collections of bibliographic references.
The systems and processes involved in the establishment, support, management, and operation of registers, e.g., disease registers.
Studies in which variables relating to an individual or group of individuals are assessed over a period of time.
Works about clinical trials that involve at least one test treatment and one control treatment, concurrent enrollment and follow-up of the test- and control-treated groups, and in which the treatments to be administered are selected by a random process, such as the use of a random-numbers table.
Inhaling and exhaling the smoke of burning TOBACCO.
Social and economic factors that characterize the individual or group within the social structure.
Based on known statistical data, the number of years which any person of a given age may reasonably expected to live.
Statistical interpretation and description of a population with reference to distribution, composition, or structure.
The external elements and conditions which surround, influence, and affect the life and development of an organism or population.
The concept pertaining to the health status of inhabitants of the world.
The pattern of any process, or the interrelationship of phenomena, which affects growth or change within a population.
Includes the spectrum of human immunodeficiency virus infections that range from asymptomatic seropositivity, thru AIDS-related complex (ARC), to acquired immunodeficiency syndrome (AIDS).
Any substance in the air which could, if present in high enough concentration, harm humans, animals, vegetation or material. Substances include GASES; PARTICULATE MATTER; and volatile ORGANIC CHEMICALS.
Great Britain is not a medical term, but a geographical name for the largest island in the British Isles, which comprises England, Scotland, and Wales, forming the major part of the United Kingdom.
All deaths reported in a given population.
New abnormal growth of tissue. Malignant neoplasms show a greater degree of anaplasia and have the properties of invasion and metastasis, compared to benign neoplasms.
I'm sorry for any confusion, but 'Europe' is a geographical continent and not a medical term; therefore, it doesn't have a medical definition.
The prediction or projection of the nature of future problems or existing conditions based upon the extrapolation or interpretation of existing scientific data or by the application of scientific methodology.
Regular course of eating and drinking adopted by a person or animal.
Studies designed to examine associations, commonly, hypothesized causal relations. They are usually concerned with identifying or measuring the effects of risk factors or exposures. The common types of analytic study are CASE-CONTROL STUDIES; COHORT STUDIES; and CROSS-SECTIONAL STUDIES.
A quantitative method of combining the results of independent studies (usually drawn from the published literature) and synthesizing summaries and conclusions which may be used to evaluate therapeutic effectiveness, plan new studies, etc., with application chiefly in the areas of research and medicine.
The introduction of error due to systematic differences in the characteristics between those selected and those not selected for a given study. In sampling bias, error is the result of failure to ensure that all members of the reference population have a known chance of selection in the sample.
Factors that can cause or prevent the outcome of interest, are not intermediate variables, and are not associated with the factor(s) under investigation. They give rise to situations in which the effects of two processes are not separated, or the contribution of causal factors cannot be separated, or the measure of the effect of exposure or risk is distorted because of its association with other factors influencing the outcome of the study.
A set of techniques used when variation in several variables has to be studied simultaneously. In statistics, multivariate analysis is interpreted as any analytic method that allows simultaneous study of two or more dependent variables.
A multistage process that includes cloning, physical mapping, subcloning, determination of the DNA SEQUENCE, and information analysis.
A functional system which includes the organisms of a natural community together with their environment. (McGraw Hill Dictionary of Scientific and Technical Terms, 4th ed)
The measurement of radiation by photography, as in x-ray film and film badge, by Geiger-Mueller tube, and by SCINTILLATION COUNTING.
The largest country in North America, comprising 10 provinces and three territories. Its capital is Ottawa.
A technique of inputting two-dimensional images into a computer and then enhancing or analyzing the imagery into a form that is more useful to the human observer.
Determination, by measurement or comparison with a standard, of the correct value of each scale reading on a meter or other measuring instrument; or determination of the settings of a control device that correspond to particular values of voltage, current, frequency or other output.
Individuals whose ancestral origins are in the continent of Europe.
The mating of plants or non-human animals which are closely related genetically.
I'm sorry for any confusion, but "Brazil" is not a medical term or concept, it is a country located in South America, known officially as the Federative Republic of Brazil. If you have any questions related to health, medicine, or science, I'd be happy to help answer those!
The complete summaries of the frequencies of the values or categories of a measurement made on a group of items, a population, or other collection of data. The distribution tells either how many or what proportion of the group was found to have each value (or each range of values) out of all the possible values that the quantitative measure can have.
The outward appearance of the individual. It is the product of interactions between genes, and between the GENOTYPE and the environment.
The proportion of one particular in the total of all ALLELES for one genetic locus in a breeding POPULATION.
Permanent deprivation of breast milk and commencement of nourishment with other food. (From Stedman, 25th ed)
Methods developed to aid in the interpretation of ultrasound, radiographic images, etc., for diagnosis of disease.
Remains, impressions, or traces of animals or plants of past geological times which have been preserved in the earth's crust.
A phenotypically recognizable genetic trait which can be used to identify a genetic locus, a linkage group, or a recombination event.
Organized periodic procedures performed on large groups of people for the purpose of detecting disease.
Devices or objects in various imaging techniques used to visualize or enhance visualization by simulating conditions encountered in the procedure. Phantoms are used very often in procedures employing or measuring x-irradiation or radioactive material to evaluate performance. Phantoms often have properties similar to human tissue. Water demonstrates absorbing properties similar to normal tissue, hence water-filled phantoms are used to map radiation levels. Phantoms are used also as teaching aids to simulate real conditions with x-ray or ultrasonic machines. (From Iturralde, Dictionary and Handbook of Nuclear Medicine and Clinical Imaging, 1990)
The confinement of a patient in a hospital.
Variant forms of the same gene, occupying the same locus on homologous CHROMOSOMES, and governing the variants in production of the same gene product.
The range or frequency distribution of a measurement in a population (of organisms, organs or things) that has not been selected for the presence of disease or abnormality.
The restriction of a characteristic behavior, anatomical structure or physical system, such as immune response; metabolic response, or gene or gene variant to the members of one species. It refers to that property which differentiates one species from another but it is also used for phylogenetic levels higher or lower than the species.
Tumors or cancer of the human BREAST.
I'm sorry for any confusion, but "California" is a place, specifically a state on the western coast of the United States, and not a medical term or concept. Therefore, it doesn't have a medical definition.
The rate dynamics in chemical or physical systems.

p53, cellular proliferation, and apoptosis-related factors in thymic neuroendocrine tumors. (1/10675)

Thymic neuroendocrine tumors are biologically aggressive neoplasms with extensive local invasion and high mortality. Although various markers of cellular proliferation and apoptosis have correlated with degrees of tumor differentiation in pulmonary neuroendocrine neoplasms, they have not been systematically studied in thymic neuroendocrine tumors. We immunostained 21 cases of thymic neuroendocrine tumors for p53, MIB-1, and the apoptosis-related markers Bcl-2, Bcl-x, and Bax. By histological classification the cases were low-grade (nine cases), intermediate-grade (eight cases), and high-grade (four cases) thymic neuroendocrine tumors. p53 was expressed in five cases: 1/9 low grade, 3/8 intermediate grade, and 2/4 high grade. The mean cellular proliferation (MIB-1) was 7.1% (range 2-12%) in low-grade thymic neuroendocrine tumors, 6.1% (range 2-15%) in intermediate-grade thymic neuroendocrine tumors, and 34.2% (range 2-80%) in high-grade thymic neuroendocrine tumors. Bcl-2 was expressed in 16 cases: 7/9 low grade, 5/8 intermediate grade, and 4/4 high grade. Bcl-x was expressed in 16 cases: 7/9 low grade, 6/8 intermediate grade, and 3/4 high grade. Bax was expressed in 13 cases: 5/9 low grade, 4/8 intermediate grade, and 4/4 high grade. The presence of mutant p53 in the tumor was associated with a statistically significant decreased mean survival (P<0.05). In contrast, either by positive or negative staining or by the score technique (staining intensity x percentage of cells staining), the presence of Bcl-x was associated with an increased mean survival (P<0.05). Finally, a Bcl-x : Bax ratio >or=1 was also associated with an increased mean survival, as compared to a Bcl-x : Bax ratio >or=1 (P<0.05). Our study shows that p53 expression and certain apoptosis markers correlate with survival. The expression of these markers may account for differences in biological behavior.  (+info)

Prognostic relevance of activated Akt kinase in node-negative breast cancer: a clinicopathological study of 99 cases. (2/10675)

Patients with lymphnode-negative breast cancer show a 10-year tumor recurrence rate of approximately 30%. Therefore, it is important to identify high-risk patients who would benefit from further adjuvant therapy. For this purpose, we examined the activation state of two kinases important in the regulation of cell proliferation and apoptosis in a series of 99 node-negative breast cancer cases with a mean follow-up of 10 years: Akt and extracellular regulated kinase (ERK1/2). The activation of Akt and ERK1/2 was investigated by immunohistochemistry using phospho-specific antibodies. The results were correlated with HER-2/neu expression, histological grading, receptor status, overall survival (OS) as well as with cell proliferation (Ki67 immunoreactivity, mitotic count) and tumor apoptosis assessed by TUNEL staining. Activation of Akt (pAkt) but not activation of ERK1/2 (pERK1/2) correlated with HER-2/neu overexpression (P<0.05) and was related to reduced tumor apoptosis (P<0.05). No association was found between pAkt or pERK1/2 with cell proliferation assessed by Ki67 and mitotic count (MC). Survival analysis of receptor status, HER2/neu expression, histological grading, MC and pAkt immunoexpression showed a significant correlation with decreased OS, but only pAkt reached statistical significance in the multivariate Cox regression analysis (P=0.015). Activation of Akt in node-negative breast cancer may indicate aggressive tumor behavior and may constitute an independent prognostic factor of OS. The determination of pAkt status may be of value in identifying high-risk patients, who would benefit from adjuvant therapy, and gives a rationale to investigate new therapy strategies by specific inhibition of the Akt signaling pathway in breast cancer.  (+info)

Serum nucleosomes during neoadjuvant chemotherapy in patients with cervical cancer. Predictive and prognostic significance. (3/10675)

BACKGROUND: It has been shown that free DNA circulates in serum plasma of patients with cancer and that at least part is present in the form of oligo- and monucleosomes, a marker of cell death. Preliminary data has shown a good correlation between decrease of nucleosomes with response and prognosis. Here, we performed pre- and post-chemotherapy determinations of serum nucleosomes with an enzyme-linked immunosorbent assay (ELISA) method in a group of patients with cervical cancer receiving neoadjuvant chemotherapy. METHODS: From December 2000 to June 2001, 41 patients with cervical cancer staged as FIGO stages IB2-IIIB received three 21-day courses of carboplatin and paclitaxel, both administered at day 1; then, patients underwent radical hysterectomy. Nucleosomes were measured the day before (baseline), at day seven of the first course and day seven of the third course of chemotherapy. Values of nucleosomes were analyzed with regard to pathologic response and to time to progression-free and overall survival. RESULTS: All patients completed chemotherapy, were evaluable for pathologic response, and had nucleosome levels determined. At a mean follow-up of 23 months (range, 7-26 months), projected progression time and overall survival were 80.3 and 80.4%, respectively. Mean differential values of nucleosomes were lower in the third course as compared with the first course (p >0.001). The decrease in the third course correlated with pathologic response (p = 0.041). Survival analysis showed a statistically significant, better progression-free and survival time in patients who showed lower levels at the third course (p = 0.0243 and p = 0.0260, respectively). Cox regression analysis demonstrated that nucleosome increase in the third course increased risk of death to 6.86 (95% confidence interval [CI 95%], 0.84-56.0). CONCLUSION: Serum nucleosomes may have a predictive role for response and prognostic significance in patients with cervical cancer patients treated with neoadjuvant chemotherapy.  (+info)

Prognostic risk factors in patients with interstitial lung disease referred for lung transplantation. (4/10675)

The aim of the study was to identify prognostic factors that would differentiate patients with interstitial lung disease between those with and without a chance to survive until lung transplantation. A retrospective study was performed in patients with interstitial lung disease referred for lung transplantation between September 1999 and April 2005. The analysis included the demographic data, the time from referral to transplantation, the functional tests (FVC, FEV1, FEV1%VC, the PaO(2) at rest and after oxygen supplementation via a nasal catheter), the count of NYHA functional classes, the left ventricular ejection fraction (EF), the distance covered during a 6-min walk test, and the pathogens in the respiratory tract. The patients were divided into two groups: Group 1 - lung transplant candidates who survived until the successful procedure and Group 2 - lung transplant candidates who died while on the waiting list. There were statistical differences between the two groups in PaO2 after supplementation (P=0.005), EF (P=0.002), and the 6-min walk distance (P=0.001). It appears that simple functional tests of the cardiorespiratory system may define survival of patients with interstitial lung disease waiting for lung transplantation.  (+info)

Quantitative flow cytometry of ZAP-70 levels in chronic lymphocytic leukemia using molecules of equivalent soluble fluorochrome. (5/10675)

BACKGROUND: ZAP-70 has emerged as a potential pivotal prognostic marker for patients with chronic lymphocytic leukemia (CLL), which could replace immunoglobulin heavy chain mutation status. Although several flow cytometry assays have been described for assessing ZAP-70 in CLL, certain technical and scientific issues remain unsolved, which have prevented results of this crucial test from being reported, even in the best routine flow cytometry laboratories. In this report, we aimed to solve some of these issues by providing a computerized quantitative flow cytometric assay for ZAP-70 within the entire CLL population, which would be easy to perform and enable standardization between laboratories. METHODS: Intracellular ZAP-70 levels in CLL and normal B cells were assessed by molecules of equivalent soluble fluorochrome (MESF), employing Quantum FITC MESF calibration beads to establish a standard curve relating channel value to fluorescence intensity in MESF units and the QuickCal v. 2.2 program (www.bangslabs.com) and clinical relevance of the data was determined. RESULTS: The average ZAP-70 expression value in the CD19(+)/CD5(+) cells from 35 CLL patients was 103,701 MESF when compared with 12,621 MESF in B cells from 20 normal blood samples. "Low" and "high" ZAP-70 CLL subgroups were defined. Patients with "high ZAP-70 MESF" CLL had a shorter time to disease progression (P = 0.0005) and a more advanced clinical stage (P = 0.0018) when compared with patients in the "low ZAP-70 MESF" CLL subgroup. CONCLUSIONS: This quantitative analysis method can be employed to obtain a more specific and highly accurate assessment of ZAP-70 levels in CLL cells. The method can easily be standardized, in any routine flow laboratory, thereby improving reproducibility and reliability of ZAP-70 analysis.  (+info)

Long-term outcome after Talent endograft implantation for aneurysms of the abdominal aorta: a multicenter retrospective study. (6/10675)

BACKGROUND: The development of newer-generation endografts for the endovascular treatment of abdominal aortic aneurysms has resulted in considerable improvements in clinical performance. However, long-term outcome data are still scarce. To assess long-term clinical and radiographic outcomes after use of the Talent stent graft, a retrospective analysis was performed that was based on 165 patients treated with this endograft in Germany between October 1996 and December 1998. METHODS: Data were collected according to the recommendation of the ad hoc committee for standardized reporting practices in vascular surgery and were evaluated statistically by using univariate and multivariate analyses. RESULTS: A total of 165 patients were treated with a Talent endograft in 9 German centers before December 31, 1998. Most were asymptomatic (94.5%), male (97.6%), and treated with a bifurcated graft (86.7%). Two patients (1.2%) died within 30 days, and 28 (17%) died during the follow-up period. The cause of death was aneurysm rupture in one case. Survival was 95.4% +/- 1.7% at 1 year, 89% +/- 2.6% at 2 years, 78.1% +/- 3.6% at 5 years, and 76.2% +/- 4.1% at 7 years. Patients classified as American Society of Anesthesiologists grade IV had a significantly lower survival rate (24.9%) than those classified as American Society of Anesthesiologists grade II and III (91.9% and 77.3%). During a mean follow-up period of 53.2 +/- 20.1 months (range, 1-84 months), 47 secondary procedures were performed in 31 patients (18.8%). Kaplan-Meier estimates showed a freedom from secondary intervention of 94.7% +/- 1.8%, 81.7% +/- 3.3%, and 77.4% +/- 3.6% at 1, 3, and 7 years, respectively. The reason for secondary treatment was endograft thrombosis in 10 patients (6.1%), persisting primary endoleak in 9 (5.5%), late secondary endoleak in 6 (3.6%), graft migration in 3 (1.8%), aneurysm rupture in 2 (1.2%), and graft infection in 1 (0.6%). Device migration (> or =10 mm) occurred in seven patients (4.2%). Other graft changes, such as graft kinking (n = 4; 2.4%), fracture of metallic stents (n = 2; 1.2%), erosion of the longitudinal bar (n = 2; 1.2%), or modular component separation (n = 1; 0.6%), were rare. Follow-up computed tomographic imaging revealed a decrease of the maximum aneurysm sac diameter (>5 mm) in 106 (64.2%) patients and an increase in 14 (8.5%) patients. The mean aneurysm diameter significantly decreased (P < .001). Of the factors recorded at baseline, only endoleaks showed a significant correlation with the risk of aneurysm increase during follow-up (P < .001). Adverse anatomy (neck diameter >28 mm, neck length <15 mm, and '5 patent aortic branches) did not adversely influence the aneurysm shrinkage rate, the risk for a secondary procedure, or the clinical success rate. A significantly higher rate of clinical success (P < .05) was observed in patients older than 65 years of age. CONCLUSIONS: Implantation of the Talent endograft device is a safe and effective alternative to open surgery for exclusion of abdominal aortic aneurysm. In comparison with first-generation grafts, the device showed superior durability for as long as 5 to 7 years after implantation. Even if prototypes of the Talent device were implanted in this study, the graft was also successfully used in most patients, even in those with adverse anatomy. Because improvements of the endograft have been made to address connecting bar breaks, a lower incidence of graft limb occlusion can be expected in the future.  (+info)

Carotid artery stenting in octogenarians is associated with increased adverse outcomes. (7/10675)

BACKGROUND: Carotid artery stenting is an increasingly common endovascular treatment of carotid artery stenosis advocated in high-risk patients despite reports of increased adverse periprocedural outcomes in patients aged >80 years. We sought to evaluate our single institution experience with octogenarians and whether they have an increased incidence of major complications with carotid artery stenting. METHODS: Three hundred eighty-six patients, including 260 patients from 10 regulatory trials, who underwent carotid artery stenting between June 1996 and March 2004 for symptomatic or asymptomatic carotid stenosis were reviewed from a prospectively maintained database. Periprocedural (< or =30 days after carotid artery stenting) cerebrovascular accident, transient ischemic attack, myocardial infarction, and death outcomes were compared between 87 octogenarians and 295 nonoctogenarians. Univariate and multivariate analysis was performed for confounding factors. Kaplan-Meier analysis of stroke and death outcomes was performed for a 1-year follow-up. RESULTS: All adverse outcomes were significantly higher in octogenarians compared with younger patients: 30-day stroke rate, 8.0% vs 2.7% (P = .02); 30-day stroke, myocardial infarction, or death, 9.2% vs 3.4% (P = .02). Cohorts were similar in terms of gender, comorbidities, antiplatelet medications, symptomatic status, and use of cerebral protection. Octogenarians had a greater incidence of contralateral internal carotid artery occlusion (26% vs 12%, P = .001), atrial fibrillation (21% vs 8%, P = .001), and congestive heart failure (28% vs 15%, P = .007), but a lower incidence of hypercholesterolemia (53% vs 72%, P = .001) and active smoking (8% vs 24%, P = .001). Multivariate analysis of 30-day major adverse outcomes demonstrated an association between age > or =80 and adverse outcome (odds ratio, 2.85; P = .043) as well as a protective effect of the preprocedural use of aspirin (odds ratio, 0.30, P = .027). At 1-year follow-up, only 75% of octogenarians and 87% of nonoctogenarians were free from stroke, myocardial infarction, or death (P = 005, Kaplan-Meier analysis). CONCLUSIONS: Octogenarians undergoing carotid artery stenting are at higher risk than nonoctogenarians for periprocedural complications, including neurologic events and death. Major event-free survival at 1 year is also significantly better in nonoctogenarians. These risks should be weighed when considering carotid stenting in elderly patients.  (+info)

Management of in-sent restenosis after carotid artery stenting in high-risk patients. (8/10675)

BACKGROUND: Carotid artery stenting (CAS) has emerged as an acceptable treatment alternative in patients with carotid bifurcation disease. Although early results of CAS have been promising, long-term clinical outcomes remain less certain. We report herein the frequency, management, and clinical outcome of in-stent restenosis (ISR) after CAS at a single academic institution. METHODS: Clinical records of 208 CAS procedures in 188 patients with carotid stenosis of 80% or greater, including 48 (26.5%) asymptomatic patients, during a 42-month period were analyzed. Follow-up serial carotid duplex ultrasound scans were performed. Selective angiography and repeat intervention were performed when duplex ultrasound scans showed 80% or greater ISR. Treatment outcomes of ISR interventions were analyzed. RESULTS: Over a median 17-month follow-up, 33 (15.9%) ISRs of 60% or greater were found, according to the Doppler criteria. Among them, seven patients (3.4%) with a mean age of 68 years (range, 65-87 years) developed high-grade ISR (> or =80%), and they all underwent further endovascular interventions. Six patients with high-grade ISR were asymptomatic, whereas one remaining patient presented with a transient ischemic attack. Five of seven ISRs occurred within 12 months of CAS, and two occurred at 18 months' follow-up. Treatment indications for initial CAS in these seven patients included recurrent stenosis after CEA (n = 4), radiation-induced stenosis (n = 1), and high-cardiac-risk criteria (n = 2). Treatment modalities for ISR included balloon angioplasty alone (n = 1), cutting balloon angioplasty alone (n = 4), cutting balloon angioplasty with stent placement (n = 1), and balloon angioplasty with stent placement (n = 1). Technical success was achieved in all patients, and no periprocedural complications occurred. Two patients with post-CEA restenosis developed restenosis after ISR interventions, both of whom were successfully treated with cutting balloon angioplasty at 6 and 8 months. The remaining five patients showed an absence of recurrent stenosis or symptoms during a mean follow-up of 12 months (range, 3-37 months). By using the Kaplan-Meier analysis, the freedom from 80% or greater ISR after CAS procedures at 12, 24, 36, and 42 months was 97%, 97%, 96%, and 94%, respectively. CONCLUSIONS: Our study showed that ISR after CAS remains uncommon. Successful treatment of ISR can be achieved by endovascular interventions, which incurred no instance of periprocedural complications in our series. Patients who developed ISR after CEA were likely to develop restenosis after IRS intervention. Diligent ultrasound follow-up scans are important after CAS, particularly in patients with post-CEA restenosis.  (+info)

The Kaplan-Meier estimate is a statistical method used to calculate the survival probability over time in a population. It is commonly used in medical research to analyze time-to-event data, such as the time until a patient experiences a specific event like disease progression or death. The Kaplan-Meier estimate takes into account censored data, which occurs when some individuals are lost to follow-up before experiencing the event of interest.

The method involves constructing a survival curve that shows the proportion of subjects still surviving at different time points. At each time point, the survival probability is calculated as the product of the conditional probabilities of surviving from one time point to the next. The Kaplan-Meier estimate provides an unbiased and consistent estimator of the survival function, even when censoring is present.

In summary, the Kaplan-Meier estimate is a crucial tool in medical research for analyzing time-to-event data and estimating survival probabilities over time while accounting for censored observations.

Retrospective studies, also known as retrospective research or looking back studies, are a type of observational study that examines data from the past to draw conclusions about possible causal relationships between risk factors and outcomes. In these studies, researchers analyze existing records, medical charts, or previously collected data to test a hypothesis or answer a specific research question.

Retrospective studies can be useful for generating hypotheses and identifying trends, but they have limitations compared to prospective studies, which follow participants forward in time from exposure to outcome. Retrospective studies are subject to biases such as recall bias, selection bias, and information bias, which can affect the validity of the results. Therefore, retrospective studies should be interpreted with caution and used primarily to generate hypotheses for further testing in prospective studies.

Proportional hazards models are a type of statistical analysis used in medical research to investigate the relationship between covariates (predictor variables) and survival times. The most common application of proportional hazards models is in the Cox regression model, which is named after its developer, Sir David Cox.

In a proportional hazards model, the hazard rate or risk of an event occurring at a given time is assumed to be proportional to the hazard rate of a reference group, after adjusting for the covariates. This means that the ratio of the hazard rates between any two individuals remains constant over time, regardless of their survival times.

Mathematically, the hazard function h(t) at time t for an individual with a set of covariates X can be expressed as:

h(t|X) = h0(t) \* exp(β1X1 + β2X2 + ... + βpXp)

where h0(t) is the baseline hazard function, X1, X2, ..., Xp are the covariates, and β1, β2, ..., βp are the regression coefficients that represent the effect of each covariate on the hazard rate.

The assumption of proportionality is crucial in the interpretation of the results from a Cox regression model. If the assumption is violated, then the estimated regression coefficients may be biased and misleading. Therefore, it is important to test for the proportional hazards assumption before interpreting the results of a Cox regression analysis.

Prognosis is a medical term that refers to the prediction of the likely outcome or course of a disease, including the chances of recovery or recurrence, based on the patient's symptoms, medical history, physical examination, and diagnostic tests. It is an important aspect of clinical decision-making and patient communication, as it helps doctors and patients make informed decisions about treatment options, set realistic expectations, and plan for future care.

Prognosis can be expressed in various ways, such as percentages, categories (e.g., good, fair, poor), or survival rates, depending on the nature of the disease and the available evidence. However, it is important to note that prognosis is not an exact science and may vary depending on individual factors, such as age, overall health status, and response to treatment. Therefore, it should be used as a guide rather than a definitive forecast.

Treatment outcome is a term used to describe the result or effect of medical treatment on a patient's health status. It can be measured in various ways, such as through symptoms improvement, disease remission, reduced disability, improved quality of life, or survival rates. The treatment outcome helps healthcare providers evaluate the effectiveness of a particular treatment plan and make informed decisions about future care. It is also used in clinical research to compare the efficacy of different treatments and improve patient care.

Medical survival rate is a statistical measure used to determine the percentage of patients who are still alive for a specific period of time after their diagnosis or treatment for a certain condition or disease. It is often expressed as a five-year survival rate, which refers to the proportion of people who are alive five years after their diagnosis. Survival rates can be affected by many factors, including the stage of the disease at diagnosis, the patient's age and overall health, the effectiveness of treatment, and other health conditions that the patient may have. It is important to note that survival rates are statistical estimates and do not necessarily predict an individual patient's prognosis.

In the field of medicine, "time factors" refer to the duration of symptoms or time elapsed since the onset of a medical condition, which can have significant implications for diagnosis and treatment. Understanding time factors is crucial in determining the progression of a disease, evaluating the effectiveness of treatments, and making critical decisions regarding patient care.

For example, in stroke management, "time is brain," meaning that rapid intervention within a specific time frame (usually within 4.5 hours) is essential to administering tissue plasminogen activator (tPA), a clot-busting drug that can minimize brain damage and improve patient outcomes. Similarly, in trauma care, the "golden hour" concept emphasizes the importance of providing definitive care within the first 60 minutes after injury to increase survival rates and reduce morbidity.

Time factors also play a role in monitoring the progression of chronic conditions like diabetes or heart disease, where regular follow-ups and assessments help determine appropriate treatment adjustments and prevent complications. In infectious diseases, time factors are crucial for initiating antibiotic therapy and identifying potential outbreaks to control their spread.

Overall, "time factors" encompass the significance of recognizing and acting promptly in various medical scenarios to optimize patient outcomes and provide effective care.

Follow-up studies are a type of longitudinal research that involve repeated observations or measurements of the same variables over a period of time, in order to understand their long-term effects or outcomes. In medical context, follow-up studies are often used to evaluate the safety and efficacy of medical treatments, interventions, or procedures.

In a typical follow-up study, a group of individuals (called a cohort) who have received a particular treatment or intervention are identified and then followed over time through periodic assessments or data collection. The data collected may include information on clinical outcomes, adverse events, changes in symptoms or functional status, and other relevant measures.

The results of follow-up studies can provide important insights into the long-term benefits and risks of medical interventions, as well as help to identify factors that may influence treatment effectiveness or patient outcomes. However, it is important to note that follow-up studies can be subject to various biases and limitations, such as loss to follow-up, recall bias, and changes in clinical practice over time, which must be carefully considered when interpreting the results.

The Radiation Leukemia Virus (RLV) is not a recognized medical term or a virus with clinical significance in human medicine. However, it appears to be a term used in some scientific research, particularly in the field of molecular biology and genetics, where it refers to a retrovirus that was first isolated from mice exposed to high levels of radiation.

Radiation Leukemia Virus (RLV) is a murine leukemia virus that was originally discovered in 1976 in mice that had been exposed to high doses of radiation. RLV is a retrovirus, which means it contains RNA as its genetic material and uses an enzyme called reverse transcriptase to create DNA copies of its genome that can integrate into the host cell's chromosomes.

RLV has been used in laboratory studies to investigate the mechanisms of retroviral infection, gene regulation, and tumorigenesis. However, it is not a human virus and does not cause leukemia or any other diseases in humans.

Disease-free survival (DFS) is a term used in medical research and clinical practice, particularly in the field of oncology. It refers to the length of time after primary treatment for a cancer during which no evidence of the disease can be found. This means that the patient shows no signs or symptoms of the cancer, and any imaging studies or other tests do not reveal any tumors or other indications of the disease.

DFS is often used as an important endpoint in clinical trials to evaluate the effectiveness of different treatments for cancer. By measuring the length of time until the cancer recurs or a new cancer develops, researchers can get a better sense of how well a particular treatment is working and whether it is improving patient outcomes.

It's important to note that DFS is not the same as overall survival (OS), which refers to the length of time from primary treatment until death from any cause. While DFS can provide valuable information about the effectiveness of cancer treatments, it does not necessarily reflect the impact of those treatments on patients' overall survival.

Survival analysis is a branch of statistics that deals with the analysis of time to event data. It is used to estimate the time it takes for a certain event of interest to occur, such as death, disease recurrence, or treatment failure. The event of interest is called the "failure" event, and survival analysis estimates the probability of not experiencing the failure event until a certain point in time, also known as the "survival" probability.

Survival analysis can provide important information about the effectiveness of treatments, the prognosis of patients, and the identification of risk factors associated with the event of interest. It can handle censored data, which is common in medical research where some participants may drop out or be lost to follow-up before the event of interest occurs.

Survival analysis typically involves estimating the survival function, which describes the probability of surviving beyond a certain time point, as well as hazard functions, which describe the instantaneous rate of failure at a given time point. Other important concepts in survival analysis include median survival times, restricted mean survival times, and various statistical tests to compare survival curves between groups.

A cohort study is a type of observational study in which a group of individuals who share a common characteristic or exposure are followed up over time to determine the incidence of a specific outcome or outcomes. The cohort, or group, is defined based on the exposure status (e.g., exposed vs. unexposed) and then monitored prospectively to assess for the development of new health events or conditions.

Cohort studies can be either prospective or retrospective in design. In a prospective cohort study, participants are enrolled and followed forward in time from the beginning of the study. In contrast, in a retrospective cohort study, researchers identify a cohort that has already been assembled through medical records, insurance claims, or other sources and then look back in time to assess exposure status and health outcomes.

Cohort studies are useful for establishing causality between an exposure and an outcome because they allow researchers to observe the temporal relationship between the two. They can also provide information on the incidence of a disease or condition in different populations, which can be used to inform public health policy and interventions. However, cohort studies can be expensive and time-consuming to conduct, and they may be subject to bias if participants are not representative of the population or if there is loss to follow-up.

Acetic anhydride is a chemical compound with the formula (CH3CO)2O. It is a colorless liquid that is used as a reagent in organic synthesis, particularly in the production of cellulose acetate and other acetate esters. Acetic anhydride is also an important intermediate in the synthesis of certain pharmaceuticals and dyes.

In medical terminology, acetic anhydride is not typically used as a diagnostic or therapeutic agent. However, it can be used in laboratory settings to synthesize compounds that may have medical applications. For example, acetic anhydride has been used to produce certain antiviral drugs and antibiotics.

It is important to note that acetic anhydride can be harmful or fatal if swallowed, inhaled, or absorbed through the skin. It can cause burns and eye damage, and may be harmful to the respiratory system if inhaled. Therefore, it should be handled with care and used only in well-ventilated areas with appropriate personal protective equipment.

Medical Definition:

"Risk factors" are any attribute, characteristic or exposure of an individual that increases the likelihood of developing a disease or injury. They can be divided into modifiable and non-modifiable risk factors. Modifiable risk factors are those that can be changed through lifestyle choices or medical treatment, while non-modifiable risk factors are inherent traits such as age, gender, or genetic predisposition. Examples of modifiable risk factors include smoking, alcohol consumption, physical inactivity, and unhealthy diet, while non-modifiable risk factors include age, sex, and family history. It is important to note that having a risk factor does not guarantee that a person will develop the disease, but rather indicates an increased susceptibility.

Rhodobacter sphaeroides is not a medical term, but rather a scientific name for a type of bacteria. It belongs to the class of proteobacteria and is commonly found in soil, fresh water, and the ocean. This bacterium is capable of photosynthesis, and it can use light as an energy source, converting it into chemical energy. Rhodobacter sphaeroides is often studied in research settings due to its unique metabolic capabilities and potential applications in biotechnology.

In a medical context, Rhodobacter sphaeroides may be mentioned in relation to rare cases of infection, particularly in individuals with weakened immune systems. However, it is not considered a significant human pathogen, and there are no specific medical definitions associated with this bacterium.

Neoplasm staging is a systematic process used in medicine to describe the extent of spread of a cancer, including the size and location of the original (primary) tumor and whether it has metastasized (spread) to other parts of the body. The most widely accepted system for this purpose is the TNM classification system developed by the American Joint Committee on Cancer (AJCC) and the Union for International Cancer Control (UICC).

In this system, T stands for tumor, and it describes the size and extent of the primary tumor. N stands for nodes, and it indicates whether the cancer has spread to nearby lymph nodes. M stands for metastasis, and it shows whether the cancer has spread to distant parts of the body.

Each letter is followed by a number that provides more details about the extent of the disease. For example, a T1N0M0 cancer means that the primary tumor is small and has not spread to nearby lymph nodes or distant sites. The higher the numbers, the more advanced the cancer.

Staging helps doctors determine the most appropriate treatment for each patient and estimate the patient's prognosis. It is an essential tool for communication among members of the healthcare team and for comparing outcomes of treatments in clinical trials.

Herpesvirus 1, Suid (Suid Herpesvirus 1 or SHV-1), also known as Pseudorabies Virus (PrV), is a species of the genus Varicellovirus in the subfamily Alphaherpesvirinae of the family Herpesviridae. It is a double-stranded DNA virus that primarily infects members of the Suidae family, including domestic pigs and wild boars. The virus can cause a range of symptoms known as Aujeszky's disease in these animals, which may include respiratory distress, neurological issues, and reproductive failures.

SHV-1 is highly contagious and can be transmitted through direct contact with infected animals or their secretions, as well as through aerosol transmission. Although it does not typically infect humans, there have been rare cases of human infection, usually resulting from exposure to infected pigs or their tissues. In these instances, the virus may cause mild flu-like symptoms or more severe neurological issues.

SHV-1 is an important pathogen in the swine industry and has significant economic implications due to its impact on animal health and production. Vaccination programs are widely used to control the spread of the virus and protect susceptible pig populations.

Prospective studies, also known as longitudinal studies, are a type of cohort study in which data is collected forward in time, following a group of individuals who share a common characteristic or exposure over a period of time. The researchers clearly define the study population and exposure of interest at the beginning of the study and follow up with the participants to determine the outcomes that develop over time. This type of study design allows for the investigation of causal relationships between exposures and outcomes, as well as the identification of risk factors and the estimation of disease incidence rates. Prospective studies are particularly useful in epidemiology and medical research when studying diseases with long latency periods or rare outcomes.

Molecular sequence data refers to the specific arrangement of molecules, most commonly nucleotides in DNA or RNA, or amino acids in proteins, that make up a biological macromolecule. This data is generated through laboratory techniques such as sequencing, and provides information about the exact order of the constituent molecules. This data is crucial in various fields of biology, including genetics, evolution, and molecular biology, allowing for comparisons between different organisms, identification of genetic variations, and studies of gene function and regulation.

Statistical models are mathematical representations that describe the relationship between variables in a given dataset. They are used to analyze and interpret data in order to make predictions or test hypotheses about a population. In the context of medicine, statistical models can be used for various purposes such as:

1. Disease risk prediction: By analyzing demographic, clinical, and genetic data using statistical models, researchers can identify factors that contribute to an individual's risk of developing certain diseases. This information can then be used to develop personalized prevention strategies or early detection methods.

2. Clinical trial design and analysis: Statistical models are essential tools for designing and analyzing clinical trials. They help determine sample size, allocate participants to treatment groups, and assess the effectiveness and safety of interventions.

3. Epidemiological studies: Researchers use statistical models to investigate the distribution and determinants of health-related events in populations. This includes studying patterns of disease transmission, evaluating public health interventions, and estimating the burden of diseases.

4. Health services research: Statistical models are employed to analyze healthcare utilization, costs, and outcomes. This helps inform decisions about resource allocation, policy development, and quality improvement initiatives.

5. Biostatistics and bioinformatics: In these fields, statistical models are used to analyze large-scale molecular data (e.g., genomics, proteomics) to understand biological processes and identify potential therapeutic targets.

In summary, statistical models in medicine provide a framework for understanding complex relationships between variables and making informed decisions based on data-driven insights.

Reproducibility of results in a medical context refers to the ability to obtain consistent and comparable findings when a particular experiment or study is repeated, either by the same researcher or by different researchers, following the same experimental protocol. It is an essential principle in scientific research that helps to ensure the validity and reliability of research findings.

In medical research, reproducibility of results is crucial for establishing the effectiveness and safety of new treatments, interventions, or diagnostic tools. It involves conducting well-designed studies with adequate sample sizes, appropriate statistical analyses, and transparent reporting of methods and findings to allow other researchers to replicate the study and confirm or refute the results.

The lack of reproducibility in medical research has become a significant concern in recent years, as several high-profile studies have failed to produce consistent findings when replicated by other researchers. This has led to increased scrutiny of research practices and a call for greater transparency, rigor, and standardization in the conduct and reporting of medical research.

An algorithm is not a medical term, but rather a concept from computer science and mathematics. In the context of medicine, algorithms are often used to describe step-by-step procedures for diagnosing or managing medical conditions. These procedures typically involve a series of rules or decision points that help healthcare professionals make informed decisions about patient care.

For example, an algorithm for diagnosing a particular type of heart disease might involve taking a patient's medical history, performing a physical exam, ordering certain diagnostic tests, and interpreting the results in a specific way. By following this algorithm, healthcare professionals can ensure that they are using a consistent and evidence-based approach to making a diagnosis.

Algorithms can also be used to guide treatment decisions. For instance, an algorithm for managing diabetes might involve setting target blood sugar levels, recommending certain medications or lifestyle changes based on the patient's individual needs, and monitoring the patient's response to treatment over time.

Overall, algorithms are valuable tools in medicine because they help standardize clinical decision-making and ensure that patients receive high-quality care based on the latest scientific evidence.

I'm sorry for any confusion, but "United States" is a geopolitical entity, specifically the name of the country consisting of 50 states, and it is not a medical term or concept. Therefore, I can't provide a medical definition for it. If you have any questions related to health, medicine, or biology, I would be happy to try to help answer those!

A computer simulation is a process that involves creating a model of a real-world system or phenomenon on a computer and then using that model to run experiments and make predictions about how the system will behave under different conditions. In the medical field, computer simulations are used for a variety of purposes, including:

1. Training and education: Computer simulations can be used to create realistic virtual environments where medical students and professionals can practice their skills and learn new procedures without risk to actual patients. For example, surgeons may use simulation software to practice complex surgical techniques before performing them on real patients.
2. Research and development: Computer simulations can help medical researchers study the behavior of biological systems at a level of detail that would be difficult or impossible to achieve through experimental methods alone. By creating detailed models of cells, tissues, organs, or even entire organisms, researchers can use simulation software to explore how these systems function and how they respond to different stimuli.
3. Drug discovery and development: Computer simulations are an essential tool in modern drug discovery and development. By modeling the behavior of drugs at a molecular level, researchers can predict how they will interact with their targets in the body and identify potential side effects or toxicities. This information can help guide the design of new drugs and reduce the need for expensive and time-consuming clinical trials.
4. Personalized medicine: Computer simulations can be used to create personalized models of individual patients based on their unique genetic, physiological, and environmental characteristics. These models can then be used to predict how a patient will respond to different treatments and identify the most effective therapy for their specific condition.

Overall, computer simulations are a powerful tool in modern medicine, enabling researchers and clinicians to study complex systems and make predictions about how they will behave under a wide range of conditions. By providing insights into the behavior of biological systems at a level of detail that would be difficult or impossible to achieve through experimental methods alone, computer simulations are helping to advance our understanding of human health and disease.

Prevalence, in medical terms, refers to the total number of people in a given population who have a particular disease or condition at a specific point in time, or over a specified period. It is typically expressed as a percentage or a ratio of the number of cases to the size of the population. Prevalence differs from incidence, which measures the number of new cases that develop during a certain period.

Genetic models are theoretical frameworks used in genetics to describe and explain the inheritance patterns and genetic architecture of traits, diseases, or phenomena. These models are based on mathematical equations and statistical methods that incorporate information about gene frequencies, modes of inheritance, and the effects of environmental factors. They can be used to predict the probability of certain genetic outcomes, to understand the genetic basis of complex traits, and to inform medical management and treatment decisions.

There are several types of genetic models, including:

1. Mendelian models: These models describe the inheritance patterns of simple genetic traits that follow Mendel's laws of segregation and independent assortment. Examples include autosomal dominant, autosomal recessive, and X-linked inheritance.
2. Complex trait models: These models describe the inheritance patterns of complex traits that are influenced by multiple genes and environmental factors. Examples include heart disease, diabetes, and cancer.
3. Population genetics models: These models describe the distribution and frequency of genetic variants within populations over time. They can be used to study evolutionary processes, such as natural selection and genetic drift.
4. Quantitative genetics models: These models describe the relationship between genetic variation and phenotypic variation in continuous traits, such as height or IQ. They can be used to estimate heritability and to identify quantitative trait loci (QTLs) that contribute to trait variation.
5. Statistical genetics models: These models use statistical methods to analyze genetic data and infer the presence of genetic associations or linkage. They can be used to identify genetic risk factors for diseases or traits.

Overall, genetic models are essential tools in genetics research and medical genetics, as they allow researchers to make predictions about genetic outcomes, test hypotheses about the genetic basis of traits and diseases, and develop strategies for prevention, diagnosis, and treatment.

"Likelihood functions" is a statistical concept that is used in medical research and other fields to estimate the probability of obtaining a given set of data, given a set of assumptions or parameters. In other words, it is a function that describes how likely it is to observe a particular outcome or result, based on a set of model parameters.

More formally, if we have a statistical model that depends on a set of parameters θ, and we observe some data x, then the likelihood function is defined as:

L(θ | x) = P(x | θ)

This means that the likelihood function describes the probability of observing the data x, given a particular value of the parameter vector θ. By convention, the likelihood function is often expressed as a function of the parameters, rather than the data, so we might instead write:

L(θ) = P(x | θ)

The likelihood function can be used to estimate the values of the model parameters that are most consistent with the observed data. This is typically done by finding the value of θ that maximizes the likelihood function, which is known as the maximum likelihood estimator (MLE). The MLE has many desirable statistical properties, including consistency, efficiency, and asymptotic normality.

In medical research, likelihood functions are often used in the context of Bayesian analysis, where they are combined with prior distributions over the model parameters to obtain posterior distributions that reflect both the observed data and prior knowledge or assumptions about the parameter values. This approach is particularly useful when there is uncertainty or ambiguity about the true value of the parameters, as it allows researchers to incorporate this uncertainty into their analyses in a principled way.

Bayes' theorem, also known as Bayes' rule or Bayes' formula, is a fundamental principle in the field of statistics and probability theory. It describes how to update the probability of a hypothesis based on new evidence or data. The theorem is named after Reverend Thomas Bayes, who first formulated it in the 18th century.

In mathematical terms, Bayes' theorem states that the posterior probability of a hypothesis (H) given some observed evidence (E) is proportional to the product of the prior probability of the hypothesis (P(H)) and the likelihood of observing the evidence given the hypothesis (P(E|H)):

Posterior Probability = P(H|E) = [P(E|H) x P(H)] / P(E)

Where:

* P(H|E): The posterior probability of the hypothesis H after observing evidence E. This is the probability we want to calculate.
* P(E|H): The likelihood of observing evidence E given that the hypothesis H is true.
* P(H): The prior probability of the hypothesis H before observing any evidence.
* P(E): The marginal likelihood or probability of observing evidence E, regardless of whether the hypothesis H is true or not. This value can be calculated as the sum of the products of the likelihood and prior probability for all possible hypotheses: P(E) = Σ[P(E|Hi) x P(Hi)]

Bayes' theorem has many applications in various fields, including medicine, where it can be used to update the probability of a disease diagnosis based on test results or other clinical findings. It is also widely used in machine learning and artificial intelligence algorithms for probabilistic reasoning and decision making under uncertainty.

Biological models, also known as physiological models or organismal models, are simplified representations of biological systems, processes, or mechanisms that are used to understand and explain the underlying principles and relationships. These models can be theoretical (conceptual or mathematical) or physical (such as anatomical models, cell cultures, or animal models). They are widely used in biomedical research to study various phenomena, including disease pathophysiology, drug action, and therapeutic interventions.

Examples of biological models include:

1. Mathematical models: These use mathematical equations and formulas to describe complex biological systems or processes, such as population dynamics, metabolic pathways, or gene regulation networks. They can help predict the behavior of these systems under different conditions and test hypotheses about their underlying mechanisms.
2. Cell cultures: These are collections of cells grown in a controlled environment, typically in a laboratory dish or flask. They can be used to study cellular processes, such as signal transduction, gene expression, or metabolism, and to test the effects of drugs or other treatments on these processes.
3. Animal models: These are living organisms, usually vertebrates like mice, rats, or non-human primates, that are used to study various aspects of human biology and disease. They can provide valuable insights into the pathophysiology of diseases, the mechanisms of drug action, and the safety and efficacy of new therapies.
4. Anatomical models: These are physical representations of biological structures or systems, such as plastic models of organs or tissues, that can be used for educational purposes or to plan surgical procedures. They can also serve as a basis for developing more sophisticated models, such as computer simulations or 3D-printed replicas.

Overall, biological models play a crucial role in advancing our understanding of biology and medicine, helping to identify new targets for therapeutic intervention, develop novel drugs and treatments, and improve human health.

The term "Theoretical Models" is used in various scientific fields, including medicine, to describe a representation of a complex system or phenomenon. It is a simplified framework that explains how different components of the system interact with each other and how they contribute to the overall behavior of the system. Theoretical models are often used in medical research to understand and predict the outcomes of diseases, treatments, or public health interventions.

A theoretical model can take many forms, such as mathematical equations, computer simulations, or conceptual diagrams. It is based on a set of assumptions and hypotheses about the underlying mechanisms that drive the system. By manipulating these variables and observing the effects on the model's output, researchers can test their assumptions and generate new insights into the system's behavior.

Theoretical models are useful for medical research because they allow scientists to explore complex systems in a controlled and systematic way. They can help identify key drivers of disease or treatment outcomes, inform the design of clinical trials, and guide the development of new interventions. However, it is important to recognize that theoretical models are simplifications of reality and may not capture all the nuances and complexities of real-world systems. Therefore, they should be used in conjunction with other forms of evidence, such as experimental data and observational studies, to inform medical decision-making.

Statistical data interpretation involves analyzing and interpreting numerical data in order to identify trends, patterns, and relationships. This process often involves the use of statistical methods and tools to organize, summarize, and draw conclusions from the data. The goal is to extract meaningful insights that can inform decision-making, hypothesis testing, or further research.

In medical contexts, statistical data interpretation is used to analyze and make sense of large sets of clinical data, such as patient outcomes, treatment effectiveness, or disease prevalence. This information can help healthcare professionals and researchers better understand the relationships between various factors that impact health outcomes, develop more effective treatments, and identify areas for further study.

Some common statistical methods used in data interpretation include descriptive statistics (e.g., mean, median, mode), inferential statistics (e.g., hypothesis testing, confidence intervals), and regression analysis (e.g., linear, logistic). These methods can help medical professionals identify patterns and trends in the data, assess the significance of their findings, and make evidence-based recommendations for patient care or public health policy.

In epidemiology, the incidence of a disease is defined as the number of new cases of that disease within a specific population over a certain period of time. It is typically expressed as a rate, with the number of new cases in the numerator and the size of the population at risk in the denominator. Incidence provides information about the risk of developing a disease during a given time period and can be used to compare disease rates between different populations or to monitor trends in disease occurrence over time.

Regression analysis is a statistical technique used in medicine, as well as in other fields, to examine the relationship between one or more independent variables (predictors) and a dependent variable (outcome). It allows for the estimation of the average change in the outcome variable associated with a one-unit change in an independent variable, while controlling for the effects of other independent variables. This technique is often used to identify risk factors for diseases or to evaluate the effectiveness of medical interventions. In medical research, regression analysis can be used to adjust for potential confounding variables and to quantify the relationship between exposures and health outcomes. It can also be used in predictive modeling to estimate the probability of a particular outcome based on multiple predictors.

Risk assessment in the medical context refers to the process of identifying, evaluating, and prioritizing risks to patients, healthcare workers, or the community related to healthcare delivery. It involves determining the likelihood and potential impact of adverse events or hazards, such as infectious diseases, medication errors, or medical devices failures, and implementing measures to mitigate or manage those risks. The goal of risk assessment is to promote safe and high-quality care by identifying areas for improvement and taking action to minimize harm.

In medical terms, "breeding" is not a term that is commonly used. It is more frequently used in the context of animal husbandry to refer to the process of mating animals in order to produce offspring with specific desired traits or characteristics. In human medicine, the term is not typically applied to people and instead, related concepts such as reproduction, conception, or pregnancy are used.

Genetic variation refers to the differences in DNA sequences among individuals and populations. These variations can result from mutations, genetic recombination, or gene flow between populations. Genetic variation is essential for evolution by providing the raw material upon which natural selection acts. It can occur within a single gene, between different genes, or at larger scales, such as differences in the number of chromosomes or entire sets of chromosomes. The study of genetic variation is crucial in understanding the genetic basis of diseases and traits, as well as the evolutionary history and relationships among species.

I'm sorry for any confusion, but the "Monte Carlo method" is actually a term from the field of mathematics and computer science, not medicine. It refers to a statistical technique that allows for the modeling of complex systems by running multiple simulations with random inputs. This method is widely used in various fields such as physics, engineering, and finance, but it is not a medical concept or term.

"Age factors" refer to the effects, changes, or differences that age can have on various aspects of health, disease, and medical care. These factors can encompass a wide range of issues, including:

1. Physiological changes: As people age, their bodies undergo numerous physical changes that can affect how they respond to medications, illnesses, and medical procedures. For example, older adults may be more sensitive to certain drugs or have weaker immune systems, making them more susceptible to infections.
2. Chronic conditions: Age is a significant risk factor for many chronic diseases, such as heart disease, diabetes, cancer, and arthritis. As a result, age-related medical issues are common and can impact treatment decisions and outcomes.
3. Cognitive decline: Aging can also lead to cognitive changes, including memory loss and decreased decision-making abilities. These changes can affect a person's ability to understand and comply with medical instructions, leading to potential complications in their care.
4. Functional limitations: Older adults may experience physical limitations that impact their mobility, strength, and balance, increasing the risk of falls and other injuries. These limitations can also make it more challenging for them to perform daily activities, such as bathing, dressing, or cooking.
5. Social determinants: Age-related factors, such as social isolation, poverty, and lack of access to transportation, can impact a person's ability to obtain necessary medical care and affect their overall health outcomes.

Understanding age factors is critical for healthcare providers to deliver high-quality, patient-centered care that addresses the unique needs and challenges of older adults. By taking these factors into account, healthcare providers can develop personalized treatment plans that consider a person's age, physical condition, cognitive abilities, and social circumstances.

Sensitivity and specificity are statistical measures used to describe the performance of a diagnostic test or screening tool in identifying true positive and true negative results.

* Sensitivity refers to the proportion of people who have a particular condition (true positives) who are correctly identified by the test. It is also known as the "true positive rate" or "recall." A highly sensitive test will identify most or all of the people with the condition, but may also produce more false positives.
* Specificity refers to the proportion of people who do not have a particular condition (true negatives) who are correctly identified by the test. It is also known as the "true negative rate." A highly specific test will identify most or all of the people without the condition, but may also produce more false negatives.

In medical testing, both sensitivity and specificity are important considerations when evaluating a diagnostic test. High sensitivity is desirable for screening tests that aim to identify as many cases of a condition as possible, while high specificity is desirable for confirmatory tests that aim to rule out the condition in people who do not have it.

It's worth noting that sensitivity and specificity are often influenced by factors such as the prevalence of the condition in the population being tested, the threshold used to define a positive result, and the reliability and validity of the test itself. Therefore, it's important to consider these factors when interpreting the results of a diagnostic test.

In the context of medicine and healthcare, 'probability' does not have a specific medical definition. However, in general terms, probability is a branch of mathematics that deals with the study of numerical quantities called probabilities, which are assigned to events or sets of events. Probability is a measure of the likelihood that an event will occur. It is usually expressed as a number between 0 and 1, where 0 indicates that the event is impossible and 1 indicates that the event is certain to occur.

In medical research and statistics, probability is often used to quantify the uncertainty associated with statistical estimates or hypotheses. For example, a p-value is a probability that measures the strength of evidence against a hypothesis. A small p-value (typically less than 0.05) suggests that the observed data are unlikely under the assumption of the null hypothesis, and therefore provides evidence in favor of an alternative hypothesis.

Probability theory is also used to model complex systems and processes in medicine, such as disease transmission dynamics or the effectiveness of medical interventions. By quantifying the uncertainty associated with these models, researchers can make more informed decisions about healthcare policies and practices.

I'm sorry for any confusion, but "Linear Models" is actually a term from the field of statistics and machine learning, rather than medicine. A linear model is a type of statistical model that is used to analyze the relationship between two or more variables. In a linear model, the relationship between the dependent variable (the outcome or result) and the independent variable(s) (the factors being studied) is assumed to be linear, meaning that it can be described by a straight line on a graph.

The equation for a simple linear model with one independent variable (x) and one dependent variable (y) looks like this:

y = β0 + β1*x + ε

In this equation, β0 is the y-intercept or the value of y when x equals zero, β1 is the slope or the change in y for each unit increase in x, and ε is the error term or the difference between the actual values of y and the predicted values of y based on the linear model.

Linear models are widely used in medical research to study the relationship between various factors (such as exposure to a risk factor or treatment) and health outcomes (such as disease incidence or mortality). They can also be used to adjust for confounding variables, which are factors that may influence both the independent variable and the dependent variable, and thus affect the observed relationship between them.

A newborn infant is a baby who is within the first 28 days of life. This period is also referred to as the neonatal period. Newborns require specialized care and attention due to their immature bodily systems and increased vulnerability to various health issues. They are closely monitored for signs of well-being, growth, and development during this critical time.

Population surveillance in a public health and medical context refers to the ongoing, systematic collection, analysis, interpretation, and dissemination of health-related data for a defined population over time. It aims to monitor the health status, identify emerging health threats or trends, and evaluate the impact of interventions within that population. This information is used to inform public health policy, prioritize healthcare resources, and guide disease prevention and control efforts. Population surveillance can involve various data sources, such as vital records, disease registries, surveys, and electronic health records.

Cost-benefit analysis (CBA) is a systematic process used to compare the costs and benefits of different options to determine which one provides the greatest net benefit. In a medical context, CBA can be used to evaluate the value of medical interventions, treatments, or policies by estimating and monetizing all the relevant costs and benefits associated with each option.

The costs included in a CBA may include direct costs such as the cost of the intervention or treatment itself, as well as indirect costs such as lost productivity or time away from work. Benefits may include improved health outcomes, reduced morbidity or mortality, and increased quality of life.

Once all the relevant costs and benefits have been identified and quantified, they are typically expressed in monetary terms to allow for a direct comparison. The option with the highest net benefit (i.e., the difference between total benefits and total costs) is considered the most cost-effective.

It's important to note that CBA has some limitations and can be subject to various biases and assumptions, so it should be used in conjunction with other evaluation methods to ensure a comprehensive understanding of the value of medical interventions or policies.

"Age distribution" is a term used to describe the number of individuals within a population or sample that fall into different age categories. It is often presented in the form of a graph, table, or chart, and can provide important information about the demographic structure of a population.

The age distribution of a population can be influenced by a variety of factors, including birth rates, mortality rates, migration patterns, and aging. Public health officials and researchers use age distribution data to inform policies and programs related to healthcare, social services, and other areas that affect the well-being of populations.

For example, an age distribution graph might show a larger number of individuals in the younger age categories, indicating a population with a high birth rate. Alternatively, it might show a larger number of individuals in the older age categories, indicating a population with a high life expectancy or an aging population. Understanding the age distribution of a population can help policymakers plan for future needs and allocate resources more effectively.

Pregnancy is a physiological state or condition where a fertilized egg (zygote) successfully implants and grows in the uterus of a woman, leading to the development of an embryo and finally a fetus. This process typically spans approximately 40 weeks, divided into three trimesters, and culminates in childbirth. Throughout this period, numerous hormonal and physical changes occur to support the growing offspring, including uterine enlargement, breast development, and various maternal adaptations to ensure the fetus's optimal growth and well-being.

A cross-sectional study is a type of observational research design that examines the relationship between variables at one point in time. It provides a snapshot or a "cross-section" of the population at a particular moment, allowing researchers to estimate the prevalence of a disease or condition and identify potential risk factors or associations.

In a cross-sectional study, data is collected from a sample of participants at a single time point, and the variables of interest are measured simultaneously. This design can be used to investigate the association between exposure and outcome, but it cannot establish causality because it does not follow changes over time.

Cross-sectional studies can be conducted using various data collection methods, such as surveys, interviews, or medical examinations. They are often used in epidemiology to estimate the prevalence of a disease or condition in a population and to identify potential risk factors that may contribute to its development. However, because cross-sectional studies only provide a snapshot of the population at one point in time, they cannot account for changes over time or determine whether exposure preceded the outcome.

Therefore, while cross-sectional studies can be useful for generating hypotheses and identifying potential associations between variables, further research using other study designs, such as cohort or case-control studies, is necessary to establish causality and confirm any findings.

Epidemiologic methods are systematic approaches used to investigate and understand the distribution, determinants, and outcomes of health-related events or diseases in a population. These methods are applied to study the patterns of disease occurrence and transmission, identify risk factors and causes, and evaluate interventions for prevention and control. The core components of epidemiologic methods include:

1. Descriptive Epidemiology: This involves the systematic collection and analysis of data on the who, what, when, and where of health events to describe their distribution in a population. It includes measures such as incidence, prevalence, mortality, and morbidity rates, as well as geographic and temporal patterns.

2. Analytical Epidemiology: This involves the use of statistical methods to examine associations between potential risk factors and health outcomes. It includes observational studies (cohort, case-control, cross-sectional) and experimental studies (randomized controlled trials). The goal is to identify causal relationships and quantify the strength of associations.

3. Experimental Epidemiology: This involves the design and implementation of interventions or experiments to test hypotheses about disease prevention and control. It includes randomized controlled trials, community trials, and other experimental study designs.

4. Surveillance and Monitoring: This involves ongoing systematic collection, analysis, and interpretation of health-related data for early detection, tracking, and response to health events or diseases.

5. Ethical Considerations: Epidemiologic studies must adhere to ethical principles such as respect for autonomy, beneficence, non-maleficence, and justice. This includes obtaining informed consent, ensuring confidentiality, and minimizing harm to study participants.

Overall, epidemiologic methods provide a framework for investigating and understanding the complex interplay between host, agent, and environmental factors that contribute to the occurrence of health-related events or diseases in populations.

Population Genetics is a subfield of genetics that deals with the genetic composition of populations and how this composition changes over time. It involves the study of the frequency and distribution of genes and genetic variations in populations, as well as the evolutionary forces that contribute to these patterns, such as mutation, gene flow, genetic drift, and natural selection.

Population genetics can provide insights into a wide range of topics, including the history and relationships between populations, the genetic basis of diseases and other traits, and the potential impacts of environmental changes on genetic diversity. This field is important for understanding evolutionary processes at the population level and has applications in areas such as conservation biology, medical genetics, and forensic science.

Phylogeny is the evolutionary history and relationship among biological entities, such as species or genes, based on their shared characteristics. In other words, it refers to the branching pattern of evolution that shows how various organisms have descended from a common ancestor over time. Phylogenetic analysis involves constructing a tree-like diagram called a phylogenetic tree, which depicts the inferred evolutionary relationships among organisms or genes based on molecular sequence data or other types of characters. This information is crucial for understanding the diversity and distribution of life on Earth, as well as for studying the emergence and spread of diseases.

Health surveys are research studies that collect data from a sample population to describe the current health status, health behaviors, and healthcare utilization of a particular group or community. These surveys may include questions about various aspects of health such as physical health, mental health, chronic conditions, lifestyle habits, access to healthcare services, and demographic information. The data collected from health surveys can be used to monitor trends in health over time, identify disparities in health outcomes, develop and evaluate public health programs and policies, and inform resource allocation decisions. Examples of national health surveys include the National Health Interview Survey (NHIS) and the Behavioral Risk Factor Surveillance System (BRFSS).

Logistic models, specifically logistic regression models, are a type of statistical analysis used in medical and epidemiological research to identify the relationship between the risk of a certain health outcome or disease (dependent variable) and one or more independent variables, such as demographic factors, exposure variables, or other clinical measurements.

In contrast to linear regression models, logistic regression models are used when the dependent variable is binary or dichotomous in nature, meaning it can only take on two values, such as "disease present" or "disease absent." The model uses a logistic function to estimate the probability of the outcome based on the independent variables.

Logistic regression models are useful for identifying risk factors and estimating the strength of associations between exposures and health outcomes, adjusting for potential confounders, and predicting the probability of an outcome given certain values of the independent variables. They can also be used to develop clinical prediction rules or scores that can aid in decision-making and patient care.

A case-control study is an observational research design used to identify risk factors or causes of a disease or health outcome. In this type of study, individuals with the disease or condition (cases) are compared with similar individuals who do not have the disease or condition (controls). The exposure history or other characteristics of interest are then compared between the two groups to determine if there is an association between the exposure and the disease.

Case-control studies are often used when it is not feasible or ethical to conduct a randomized controlled trial, as they can provide valuable insights into potential causes of diseases or health outcomes in a relatively short period of time and at a lower cost than other study designs. However, because case-control studies rely on retrospective data collection, they are subject to biases such as recall bias and selection bias, which can affect the validity of the results. Therefore, it is important to carefully design and conduct case-control studies to minimize these potential sources of bias.

In the context of medicine, risk is the probability or likelihood of an adverse health effect or the occurrence of a negative event related to treatment or exposure to certain hazards. It is usually expressed as a ratio or percentage and can be influenced by various factors such as age, gender, lifestyle, genetics, and environmental conditions. Risk assessment involves identifying, quantifying, and prioritizing risks to make informed decisions about prevention, mitigation, or treatment strategies.

Data collection in the medical context refers to the systematic gathering of information relevant to a specific research question or clinical situation. This process involves identifying and recording data elements, such as demographic characteristics, medical history, physical examination findings, laboratory results, and imaging studies, from various sources including patient interviews, medical records, and diagnostic tests. The data collected is used to support clinical decision-making, inform research hypotheses, and evaluate the effectiveness of treatments or interventions. It is essential that data collection is performed in a standardized and unbiased manner to ensure the validity and reliability of the results.

A questionnaire in the medical context is a standardized, systematic, and structured tool used to gather information from individuals regarding their symptoms, medical history, lifestyle, or other health-related factors. It typically consists of a series of written questions that can be either self-administered or administered by an interviewer. Questionnaires are widely used in various areas of healthcare, including clinical research, epidemiological studies, patient care, and health services evaluation to collect data that can inform diagnosis, treatment planning, and population health management. They provide a consistent and organized method for obtaining information from large groups or individual patients, helping to ensure accurate and comprehensive data collection while minimizing bias and variability in the information gathered.

Health care costs refer to the expenses incurred for medical services, treatments, procedures, and products that are used to maintain or restore an individual's health. These costs can be categorized into several types:

1. Direct costs: These include payments made for doctor visits, hospital stays, medications, diagnostic tests, surgeries, and other medical treatments and services. Direct costs can be further divided into two subcategories:
* Out-of-pocket costs: Expenses paid directly by patients, such as co-payments, deductibles, coinsurance, and any uncovered medical services or products.
* Third-party payer costs: Expenses covered by insurance companies, government programs (like Medicare, Medicaid), or other entities that pay for health care services on behalf of patients.
2. Indirect costs: These are the expenses incurred as a result of illness or injury that indirectly impact an individual's ability to work and earn a living. Examples include lost productivity, absenteeism, reduced earning capacity, and disability benefits.
3. Non-medical costs: These are expenses related to caregiving, transportation, home modifications, assistive devices, and other non-medical services required for managing health conditions or disabilities.

Health care costs can vary significantly depending on factors such as the type of medical service, geographic location, insurance coverage, and individual health status. Understanding these costs is essential for patients, healthcare providers, policymakers, and researchers to make informed decisions about treatment options, resource allocation, and health system design.

Environmental monitoring is the systematic and ongoing surveillance, measurement, and assessment of environmental parameters, pollutants, or other stressors in order to evaluate potential impacts on human health, ecological systems, or compliance with regulatory standards. This process typically involves collecting and analyzing data from various sources, such as air, water, soil, and biota, and using this information to inform decisions related to public health, environmental protection, and resource management.

In medical terms, environmental monitoring may refer specifically to the assessment of environmental factors that can impact human health, such as air quality, water contamination, or exposure to hazardous substances. This type of monitoring is often conducted in occupational settings, where workers may be exposed to potential health hazards, as well as in community-based settings, where environmental factors may contribute to public health issues. The goal of environmental monitoring in a medical context is to identify and mitigate potential health risks associated with environmental exposures, and to promote healthy and safe environments for individuals and communities.

"Cost of Illness" is a medical-economic concept that refers to the total societal cost associated with a specific disease or health condition. It includes both direct and indirect costs. Direct costs are those that can be directly attributed to the illness, such as medical expenses for diagnosis, treatment, rehabilitation, and medications. Indirect costs include productivity losses due to morbidity (reduced efficiency while working) and mortality (lost earnings due to death). Other indirect costs may encompass expenses related to caregiving or special education needs. The Cost of Illness is often used in health policy decision-making, resource allocation, and evaluating the economic impact of diseases on society.

A confidence interval (CI) is a range of values that is likely to contain the true value of a population parameter with a certain level of confidence. It is commonly used in statistical analysis to express the uncertainty associated with estimates derived from sample data.

For example, if we calculate a 95% confidence interval for the mean height of a population based on a sample of individuals, we can say that we are 95% confident that the true population mean height falls within the calculated range. The width of the confidence interval gives us an idea of how precise our estimate is - narrower intervals indicate more precise estimates, while wider intervals suggest greater uncertainty.

Confidence intervals are typically calculated using statistical formulas that take into account the sample size, standard deviation, and level of confidence desired. They can be used to compare different groups or to evaluate the effectiveness of interventions in medical research.

Statistics, as a topic in the context of medicine and healthcare, refers to the scientific discipline that involves the collection, analysis, interpretation, and presentation of numerical data or quantifiable data in a meaningful and organized manner. It employs mathematical theories and models to draw conclusions, make predictions, and support evidence-based decision-making in various areas of medical research and practice.

Some key concepts and methods in medical statistics include:

1. Descriptive Statistics: Summarizing and visualizing data through measures of central tendency (mean, median, mode) and dispersion (range, variance, standard deviation).
2. Inferential Statistics: Drawing conclusions about a population based on a sample using hypothesis testing, confidence intervals, and statistical modeling.
3. Probability Theory: Quantifying the likelihood of events or outcomes in medical scenarios, such as diagnostic tests' sensitivity and specificity.
4. Study Designs: Planning and implementing various research study designs, including randomized controlled trials (RCTs), cohort studies, case-control studies, and cross-sectional surveys.
5. Sampling Methods: Selecting a representative sample from a population to ensure the validity and generalizability of research findings.
6. Multivariate Analysis: Examining the relationships between multiple variables simultaneously using techniques like regression analysis, factor analysis, or cluster analysis.
7. Survival Analysis: Analyzing time-to-event data, such as survival rates in clinical trials or disease progression.
8. Meta-Analysis: Systematically synthesizing and summarizing the results of multiple studies to provide a comprehensive understanding of a research question.
9. Biostatistics: A subfield of statistics that focuses on applying statistical methods to biological data, including medical research.
10. Epidemiology: The study of disease patterns in populations, which often relies on statistical methods for data analysis and interpretation.

Medical statistics is essential for evidence-based medicine, clinical decision-making, public health policy, and healthcare management. It helps researchers and practitioners evaluate the effectiveness and safety of medical interventions, assess risk factors and outcomes associated with diseases or treatments, and monitor trends in population health.

"Sex factors" is a term used in medicine and epidemiology to refer to the differences in disease incidence, prevalence, or response to treatment that are observed between males and females. These differences can be attributed to biological differences such as genetics, hormones, and anatomy, as well as social and cultural factors related to gender.

For example, some conditions such as autoimmune diseases, depression, and osteoporosis are more common in women, while others such as cardiovascular disease and certain types of cancer are more prevalent in men. Additionally, sex differences have been observed in the effectiveness and side effects of various medications and treatments.

It is important to consider sex factors in medical research and clinical practice to ensure that patients receive appropriate and effective care.

I'm sorry for any confusion, but "Markov Chains" is a term from the field of mathematics and probability theory, not medicine. Markov Chains are mathematical systems that undergo transitions from one state to another according to certain probabilistic rules. They are named after Russian mathematician Andrey Markov. These chains are used in various fields, including computer science, physics, economics, and engineering, but not commonly in medical definitions or contexts.

Molecular evolution is the process of change in the DNA sequence or protein structure over time, driven by mechanisms such as mutation, genetic drift, gene flow, and natural selection. It refers to the evolutionary study of changes in DNA, RNA, and proteins, and how these changes accumulate and lead to new species and diversity of life. Molecular evolution can be used to understand the history and relationships among different organisms, as well as the functional consequences of genetic changes.

In clinical research, sample size refers to the number of participants or observations included in a study. It is a critical aspect of study design that can impact the validity and generalizability of research findings. A larger sample size typically provides more statistical power, which means that it is more likely to detect true effects if they exist. However, increasing the sample size also increases the cost and time required for a study. Therefore, determining an appropriate sample size involves balancing statistical power with practical considerations.

The calculation of sample size depends on several factors, including the expected effect size, the variability of the outcome measure, the desired level of statistical significance, and the desired power of the study. Statistical software programs are often used to calculate sample sizes that balance these factors while minimizing the overall sample size required to detect a meaningful effect.

It is important to note that a larger sample size does not necessarily mean that a study is more rigorous or well-designed. The quality of the study's methods, including the selection of participants, the measurement of outcomes, and the analysis of data, are also critical factors that can impact the validity and generalizability of research findings.

I'm sorry for any confusion, but "geography" is not a term that has a medical definition. Geography is a field of study that deals with the location and distribution of physical and cultural features on Earth's surface, as well as how humans interact with and affect those features. It is not a concept that is typically used in a medical context. If you have any questions related to medicine or healthcare, I would be happy to try to help answer them for you!

Quality-Adjusted Life Years (QALYs) is a measure of health outcomes that combines both the quality and quantity of life lived in a single metric. It is often used in economic evaluations of healthcare interventions to estimate their value for money. QALYs are calculated by multiplying the number of years of life gained by a weighting factor that reflects the quality of life experienced during those years, typically on a scale from 0 (representing death) to 1 (representing perfect health). For example, if a healthcare intervention extends a person's life by an additional five years but they experience only 80% of full health during that time, the QALY gain would be 4 (5 x 0.8). This measure allows for comparisons to be made between different interventions and their impact on both length and quality of life.

The odds ratio (OR) is a statistical measure used in epidemiology and research to estimate the association between an exposure and an outcome. It represents the odds that an event will occur in one group versus the odds that it will occur in another group, assuming that all other factors are held constant.

In medical research, the odds ratio is often used to quantify the strength of the relationship between a risk factor (exposure) and a disease outcome. An OR of 1 indicates no association between the exposure and the outcome, while an OR greater than 1 suggests that there is a positive association between the two. Conversely, an OR less than 1 implies a negative association.

It's important to note that the odds ratio is not the same as the relative risk (RR), which compares the incidence rates of an outcome in two groups. While the OR can approximate the RR when the outcome is rare, they are not interchangeable and can lead to different conclusions about the association between an exposure and an outcome.

Environmental exposure refers to the contact of an individual with any chemical, physical, or biological agent in the environment that can cause a harmful effect on health. These exposures can occur through various pathways such as inhalation, ingestion, or skin contact. Examples of environmental exposures include air pollution, water contamination, occupational chemicals, and allergens. The duration and level of exposure, as well as the susceptibility of the individual, can all contribute to the risk of developing an adverse health effect.

Occupational exposure refers to the contact of an individual with potentially harmful chemical, physical, or biological agents as a result of their job or occupation. This can include exposure to hazardous substances such as chemicals, heavy metals, or dusts; physical agents such as noise, radiation, or ergonomic stressors; and biological agents such as viruses, bacteria, or fungi.

Occupational exposure can occur through various routes, including inhalation, skin contact, ingestion, or injection. Prolonged or repeated exposure to these hazards can increase the risk of developing acute or chronic health conditions, such as respiratory diseases, skin disorders, neurological damage, or cancer.

Employers have a legal and ethical responsibility to minimize occupational exposures through the implementation of appropriate control measures, including engineering controls, administrative controls, personal protective equipment, and training programs. Regular monitoring and surveillance of workers' health can also help identify and prevent potential health hazards in the workplace.

I'm sorry for any confusion, but "population density" is actually a term used in population geography and epidemiology, rather than medical terminology. It refers to the number of people living in a specific area or region, usually measured as the number of people per square mile or square kilometer.

However, understanding population density can be important in public health and medicine because it can influence various factors related to health outcomes and healthcare delivery, such as:

1. Disease transmission rates: Higher population densities can facilitate the spread of infectious diseases, particularly those that are transmitted through close contact between individuals.
2. Access to healthcare services: Areas with lower population density might have fewer healthcare resources and providers available, making it more challenging for residents to access necessary medical care.
3. Health disparities: Population density can contribute to health inequities, as urban areas often have better access to healthcare, education, and economic opportunities than rural areas, leading to differences in health outcomes between these populations.
4. Environmental factors: Higher population densities might lead to increased pollution, noise, and other environmental hazards that can negatively impact health.

Therefore, while "population density" is not a medical definition per se, it remains an essential concept for understanding various public health and healthcare issues.

A quantitative trait is a phenotypic characteristic that can be measured and displays continuous variation, meaning it can take on any value within a range. Examples include height, blood pressure, or biochemical measurements like cholesterol levels. These traits are usually influenced by the combined effects of multiple genes (polygenic inheritance) as well as environmental factors.

Heritability, in the context of genetics, refers to the proportion of variation in a trait that can be attributed to genetic differences among individuals in a population. It is estimated using statistical methods and ranges from 0 to 1, with higher values indicating a greater contribution of genetics to the observed phenotypic variance.

Therefore, a heritable quantitative trait would be a phenotype that shows continuous variation, influenced by multiple genes and environmental factors, and for which a significant portion of the observed variation can be attributed to genetic differences among individuals in a population.

In the context of medicine, uncertainty refers to a state of having limited knowledge or awareness about a specific medical condition, diagnosis, prognosis, treatment, or outcome in a patient. It is a common experience for healthcare professionals when making decisions due to the complexity and variability of human health and disease processes. Uncertainty can arise from various sources, such as:

1. Incomplete or ambiguous information about the patient's medical history, symptoms, examination findings, or diagnostic test results.
2. Limited scientific evidence supporting specific diagnostic or therapeutic approaches.
3. Discrepancies between different sources of information or conflicting expert opinions.
4. Variability in patients' responses to treatments and their individual preferences and values.
5. Rapidly evolving medical knowledge and technology, which can make it challenging for healthcare professionals to stay up-to-date.

Uncertainty is an inherent aspect of medical practice, and managing it effectively is crucial for providing high-quality patient care. Healthcare professionals need to communicate uncertainty openly with their patients, involve them in shared decision-making processes, and seek additional information or consultation when necessary. Embracing uncertainty can also foster curiosity, learning, and innovation in the medical field.

A research design in medical or healthcare research is a systematic plan that guides the execution and reporting of research to address a specific research question or objective. It outlines the overall strategy for collecting, analyzing, and interpreting data to draw valid conclusions. The design includes details about the type of study (e.g., experimental, observational), sampling methods, data collection techniques, data analysis approaches, and any potential sources of bias or confounding that need to be controlled for. A well-defined research design helps ensure that the results are reliable, generalizable, and relevant to the research question, ultimately contributing to evidence-based practice in medicine and healthcare.

Body weight is the measure of the force exerted on a scale or balance by an object's mass, most commonly expressed in units such as pounds (lb) or kilograms (kg). In the context of medical definitions, body weight typically refers to an individual's total weight, which includes their skeletal muscle, fat, organs, and bodily fluids.

Healthcare professionals often use body weight as a basic indicator of overall health status, as it can provide insights into various aspects of a person's health, such as nutritional status, metabolic function, and risk factors for certain diseases. For example, being significantly underweight or overweight can increase the risk of developing conditions like malnutrition, diabetes, heart disease, and certain types of cancer.

It is important to note that body weight alone may not provide a complete picture of an individual's health, as it does not account for factors such as muscle mass, bone density, or body composition. Therefore, healthcare professionals often use additional measures, such as body mass index (BMI), waist circumference, and blood tests, to assess overall health status more comprehensively.

Costs refer to the total amount of resources, such as money, time, and labor, that are expended in the provision of a medical service or treatment. Costs can be categorized into direct costs, which include expenses directly related to patient care, such as medication, supplies, and personnel; and indirect costs, which include overhead expenses, such as rent, utilities, and administrative salaries.

Cost analysis is the process of estimating and evaluating the total cost of a medical service or treatment. This involves identifying and quantifying all direct and indirect costs associated with the provision of care, and analyzing how these costs may vary based on factors such as patient volume, resource utilization, and reimbursement rates.

Cost analysis is an important tool for healthcare organizations to understand the financial implications of their operations and make informed decisions about resource allocation, pricing strategies, and quality improvement initiatives. It can also help policymakers and payers evaluate the cost-effectiveness of different treatment options and develop evidence-based guidelines for clinical practice.

I'm sorry for any confusion, but there seems to be a misunderstanding. Mathematics is not a medical term; it is a branch of science dedicated to the study of numbers, shapes, and structures. However, mathematics does have many applications in medicine, such as in modeling disease spread, analyzing medical images, or designing clinical trials. If you have any questions related to mathematics in a medical context, I'd be happy to help clarify those for you!

I'm sorry for any confusion, but "seasons" is not a term that has a medical definition. Seasons refer to the four divisions of the year (spring, summer, autumn or fall, and winter) based on the position of the earth in its orbit around the sun. If you have any questions related to health or medicine, I'd be happy to try to help answer those!

"Sex distribution" is a term used to describe the number of males and females in a study population or sample. It can be presented as a simple count, a percentage, or a ratio. This information is often used in research to identify any differences in health outcomes, disease prevalence, or response to treatment between males and females. Additionally, understanding sex distribution can help researchers ensure that their studies are representative of the general population and can inform the design of future studies.

"Cattle" is a term used in the agricultural and veterinary fields to refer to domesticated animals of the genus *Bos*, primarily *Bos taurus* (European cattle) and *Bos indicus* (Zebu). These animals are often raised for meat, milk, leather, and labor. They are also known as bovines or cows (for females), bulls (intact males), and steers/bullocks (castrated males). However, in a strict medical definition, "cattle" does not apply to humans or other animals.

Least-Squares Analysis is not a medical term, but rather a statistical method that is used in various fields including medicine. It is a way to find the best fit line or curve for a set of data points by minimizing the sum of the squared distances between the observed data points and the fitted line or curve. This method is often used in medical research to analyze data, such as fitting a regression line to a set of data points to make predictions or identify trends. The goal is to find the line or curve that most closely represents the pattern of the data, which can help researchers understand relationships between variables and make more informed decisions based on their analysis.

Economic models in the context of healthcare and medicine are theoretical frameworks used to analyze and predict the economic impact and cost-effectiveness of healthcare interventions, treatments, or policies. These models utilize clinical and epidemiological data, as well as information on resource use and costs, to estimate outcomes such as quality-adjusted life years (QALYs) gained, incremental cost-effectiveness ratios (ICERs), and budget impacts. The purpose of economic models is to inform decision-making and allocate resources in an efficient and evidence-based manner. Examples of economic models include decision tree analysis, Markov models, and simulation models.

Genotype, in genetics, refers to the complete heritable genetic makeup of an individual organism, including all of its genes. It is the set of instructions contained in an organism's DNA for the development and function of that organism. The genotype is the basis for an individual's inherited traits, and it can be contrasted with an individual's phenotype, which refers to the observable physical or biochemical characteristics of an organism that result from the expression of its genes in combination with environmental influences.

It is important to note that an individual's genotype is not necessarily identical to their genetic sequence. Some genes have multiple forms called alleles, and an individual may inherit different alleles for a given gene from each parent. The combination of alleles that an individual inherits for a particular gene is known as their genotype for that gene.

Understanding an individual's genotype can provide important information about their susceptibility to certain diseases, their response to drugs and other treatments, and their risk of passing on inherited genetic disorders to their offspring.

The Predictive Value of Tests, specifically the Positive Predictive Value (PPV) and Negative Predictive Value (NPV), are measures used in diagnostic tests to determine the probability that a positive or negative test result is correct.

Positive Predictive Value (PPV) is the proportion of patients with a positive test result who actually have the disease. It is calculated as the number of true positives divided by the total number of positive results (true positives + false positives). A higher PPV indicates that a positive test result is more likely to be a true positive, and therefore the disease is more likely to be present.

Negative Predictive Value (NPV) is the proportion of patients with a negative test result who do not have the disease. It is calculated as the number of true negatives divided by the total number of negative results (true negatives + false negatives). A higher NPV indicates that a negative test result is more likely to be a true negative, and therefore the disease is less likely to be present.

The predictive value of tests depends on the prevalence of the disease in the population being tested, as well as the sensitivity and specificity of the test. A test with high sensitivity and specificity will generally have higher predictive values than a test with low sensitivity and specificity. However, even a highly sensitive and specific test can have low predictive values if the prevalence of the disease is low in the population being tested.

I am not aware of a widely accepted medical definition for the term "software," as it is more commonly used in the context of computer science and technology. Software refers to programs, data, and instructions that are used by computers to perform various tasks. It does not have direct relevance to medical fields such as anatomy, physiology, or clinical practice. If you have any questions related to medicine or healthcare, I would be happy to try to help with those instead!

I'm sorry for any confusion, but "Models, Econometric" is not a medical term. Econometric models are statistical tools used in the field of economics to estimate economic relationships and forecast future outcomes. They are based on economic theory and use historical data to estimate the parameters of mathematical equations that describe these relationships.

If you have any questions about medical terms or concepts, I'd be happy to try to help! Just let me know what you're looking for.

Analysis of Variance (ANOVA) is a statistical technique used to compare the means of two or more groups and determine whether there are any significant differences between them. It is a way to analyze the variance in a dataset to determine whether the variability between groups is greater than the variability within groups, which can indicate that the groups are significantly different from one another.

ANOVA is based on the concept of partitioning the total variance in a dataset into two components: variance due to differences between group means (also known as "between-group variance") and variance due to differences within each group (also known as "within-group variance"). By comparing these two sources of variance, ANOVA can help researchers determine whether any observed differences between groups are statistically significant, or whether they could have occurred by chance.

ANOVA is a widely used technique in many areas of research, including biology, psychology, engineering, and business. It is often used to compare the means of two or more experimental groups, such as a treatment group and a control group, to determine whether the treatment had a significant effect. ANOVA can also be used to compare the means of different populations or subgroups within a population, to identify any differences that may exist between them.

I'm sorry for any confusion, but Poisson Distribution is actually a statistical concept rather than a medical term. Here's a general definition:

Poisson Distribution is a discrete probability distribution that expresses the probability of a given number of events occurring in a fixed interval of time or space, as long as these events occur with a known average rate and independently of each other. It is often used in fields such as physics, engineering, economics, and medical research to model rare events or low-probability phenomena.

In the context of medical research, Poisson Distribution might be used to analyze the number of adverse events that occur during a clinical trial, the frequency of disease outbreaks in a population, or the rate of successes or failures in a series of experiments.

Radiation dosage, in the context of medical physics, refers to the amount of radiation energy that is absorbed by a material or tissue, usually measured in units of Gray (Gy), where 1 Gy equals an absorption of 1 Joule of radiation energy per kilogram of matter. In the clinical setting, radiation dosage is used to plan and assess the amount of radiation delivered to a patient during treatments such as radiotherapy. It's important to note that the biological impact of radiation also depends on other factors, including the type and energy level of the radiation, as well as the sensitivity of the irradiated tissues or organs.

Genetic selection, also known as natural selection, is a fundamental mechanism of evolution. It refers to the process by which certain heritable traits become more or less common in a population over successive generations due to differential reproduction of organisms with those traits.

In genetic selection, traits that increase an individual's fitness (its ability to survive and reproduce) are more likely to be passed on to the next generation, while traits that decrease fitness are less likely to be passed on. This results in a gradual change in the distribution of traits within a population over time, leading to adaptation to the environment and potentially speciation.

Genetic selection can occur through various mechanisms, including viability selection (differential survival), fecundity selection (differences in reproductive success), and sexual selection (choices made by individuals during mating). The process of genetic selection is driven by environmental pressures, such as predation, competition for resources, and changes in the availability of food or habitat.

Biometry, also known as biometrics, is the scientific study of measurements and statistical analysis of living organisms. In a medical context, biometry is often used to refer to the measurement and analysis of physical characteristics or features of the human body, such as height, weight, blood pressure, heart rate, and other physiological variables. These measurements can be used for a variety of purposes, including diagnosis, treatment planning, monitoring disease progression, and research.

In addition to physical measurements, biometry may also refer to the use of statistical methods to analyze biological data, such as genetic information or medical images. This type of analysis can help researchers and clinicians identify patterns and trends in large datasets, and make predictions about health outcomes or treatment responses.

Overall, biometry is an important tool in modern medicine, as it allows healthcare professionals to make more informed decisions based on data and evidence.

A factual database in the medical context is a collection of organized and structured data that contains verified and accurate information related to medicine, healthcare, or health sciences. These databases serve as reliable resources for various stakeholders, including healthcare professionals, researchers, students, and patients, to access evidence-based information for making informed decisions and enhancing knowledge.

Examples of factual medical databases include:

1. PubMed: A comprehensive database of biomedical literature maintained by the US National Library of Medicine (NLM). It contains citations and abstracts from life sciences journals, books, and conference proceedings.
2. MEDLINE: A subset of PubMed, MEDLINE focuses on high-quality, peer-reviewed articles related to biomedicine and health. It is the primary component of the NLM's database and serves as a critical resource for healthcare professionals and researchers worldwide.
3. Cochrane Library: A collection of systematic reviews and meta-analyses focused on evidence-based medicine. The library aims to provide unbiased, high-quality information to support clinical decision-making and improve patient outcomes.
4. OVID: A platform that offers access to various medical and healthcare databases, including MEDLINE, Embase, and PsycINFO. It facilitates the search and retrieval of relevant literature for researchers, clinicians, and students.
5. ClinicalTrials.gov: A registry and results database of publicly and privately supported clinical studies conducted around the world. The platform aims to increase transparency and accessibility of clinical trial data for healthcare professionals, researchers, and patients.
6. UpToDate: An evidence-based, physician-authored clinical decision support resource that provides information on diagnosis, treatment, and prevention of medical conditions. It serves as a point-of-care tool for healthcare professionals to make informed decisions and improve patient care.
7. TRIP Database: A search engine designed to facilitate evidence-based medicine by providing quick access to high-quality resources, including systematic reviews, clinical guidelines, and practice recommendations.
8. National Guideline Clearinghouse (NGC): A database of evidence-based clinical practice guidelines and related documents developed through a rigorous review process. The NGC aims to provide clinicians, healthcare providers, and policymakers with reliable guidance for patient care.
9. DrugBank: A comprehensive, freely accessible online database containing detailed information about drugs, their mechanisms, interactions, and targets. It serves as a valuable resource for researchers, healthcare professionals, and students in the field of pharmacology and drug discovery.
10. Genetic Testing Registry (GTR): A database that provides centralized information about genetic tests, test developers, laboratories offering tests, and clinical validity and utility of genetic tests. It serves as a resource for healthcare professionals, researchers, and patients to make informed decisions regarding genetic testing.

A registry in the context of medicine is a collection or database of standardized information about individuals who share a certain condition or attribute, such as a disease, treatment, exposure, or demographic group. These registries are used for various purposes, including:

* Monitoring and tracking the natural history of diseases and conditions
* Evaluating the safety and effectiveness of medical treatments and interventions
* Conducting research and generating hypotheses for further study
* Providing information to patients, clinicians, and researchers
* Informing public health policy and decision-making

Registries can be established for a wide range of purposes, including disease-specific registries (such as cancer or diabetes registries), procedure-specific registries (such as joint replacement or cardiac surgery registries), and population-based registries (such as birth defects or cancer registries). Data collected in registries may include demographic information, clinical data, laboratory results, treatment details, and outcomes.

Registries can be maintained by a variety of organizations, including hospitals, clinics, academic medical centers, professional societies, government agencies, and industry. Participation in registries is often voluntary, although some registries may require informed consent from participants. Data collected in registries are typically de-identified to protect the privacy of individuals.

Longitudinal studies are a type of research design where data is collected from the same subjects repeatedly over a period of time, often years or even decades. These studies are used to establish patterns of changes and events over time, and can help researchers identify causal relationships between variables. They are particularly useful in fields such as epidemiology, psychology, and sociology, where the focus is on understanding developmental trends and the long-term effects of various factors on health and behavior.

In medical research, longitudinal studies can be used to track the progression of diseases over time, identify risk factors for certain conditions, and evaluate the effectiveness of treatments or interventions. For example, a longitudinal study might follow a group of individuals over several decades to assess their exposure to certain environmental factors and their subsequent development of chronic diseases such as cancer or heart disease. By comparing data collected at multiple time points, researchers can identify trends and correlations that may not be apparent in shorter-term studies.

Longitudinal studies have several advantages over other research designs, including their ability to establish temporal relationships between variables, track changes over time, and reduce the impact of confounding factors. However, they also have some limitations, such as the potential for attrition (loss of participants over time), which can introduce bias and affect the validity of the results. Additionally, longitudinal studies can be expensive and time-consuming to conduct, requiring significant resources and a long-term commitment from both researchers and study participants.

A randomized controlled trial (RCT) is a type of clinical study in which participants are randomly assigned to receive either the experimental intervention or the control condition, which may be a standard of care, placebo, or no treatment. The goal of an RCT is to minimize bias and ensure that the results are due to the intervention being tested rather than other factors. This design allows for a comparison between the two groups to determine if there is a significant difference in outcomes. RCTs are often considered the gold standard for evaluating the safety and efficacy of medical interventions, as they provide a high level of evidence for causal relationships between the intervention and health outcomes.

Smoking is not a medical condition, but it's a significant health risk behavior. Here is the definition from a public health perspective:

Smoking is the act of inhaling and exhaling the smoke of burning tobacco that is commonly consumed through cigarettes, pipes, and cigars. The smoke contains over 7,000 chemicals, including nicotine, tar, carbon monoxide, and numerous toxic and carcinogenic substances. These toxins contribute to a wide range of diseases and health conditions, such as lung cancer, heart disease, stroke, chronic obstructive pulmonary disease (COPD), and various other cancers, as well as adverse reproductive outcomes and negative impacts on the developing fetus during pregnancy. Smoking is highly addictive due to the nicotine content, which makes quitting smoking a significant challenge for many individuals.

Socioeconomic factors are a range of interconnected conditions and influences that affect the opportunities and resources a person or group has to maintain and improve their health and well-being. These factors include:

1. Economic stability: This includes employment status, job security, income level, and poverty status. Lower income and lack of employment are associated with poorer health outcomes.
2. Education: Higher levels of education are generally associated with better health outcomes. Education can affect a person's ability to access and understand health information, as well as their ability to navigate the healthcare system.
3. Social and community context: This includes factors such as social support networks, discrimination, and community safety. Strong social supports and positive community connections are associated with better health outcomes, while discrimination and lack of safety can negatively impact health.
4. Healthcare access and quality: Access to affordable, high-quality healthcare is an important socioeconomic factor that can significantly impact a person's health. Factors such as insurance status, availability of providers, and cultural competency of healthcare systems can all affect healthcare access and quality.
5. Neighborhood and built environment: The physical conditions in which people live, work, and play can also impact their health. Factors such as housing quality, transportation options, availability of healthy foods, and exposure to environmental hazards can all influence health outcomes.

Socioeconomic factors are often interrelated and can have a cumulative effect on health outcomes. For example, someone who lives in a low-income neighborhood with limited access to healthy foods and safe parks may also face challenges related to employment, education, and healthcare access that further impact their health. Addressing socioeconomic factors is an important part of promoting health equity and reducing health disparities.

Life expectancy is a statistical measure that indicates the average amount of time a person is expected to live, based on their current age and other demographic factors such as sex, health status, and geographical location. It is often calculated using data from population studies and represents the number of years of life remaining at a given age, assuming that current mortality rates continue to apply.

For example, if the life expectancy at birth in a particular population is 80 years, it means that on average, newborns in that population are expected to live to be 80 years old. However, it's important to note that life expectancy is a statistical measure and does not predict the exact lifespan of any individual person.

Demography is the statistical study of populations, particularly in terms of size, distribution, and characteristics such as age, race, gender, and occupation. In medical contexts, demography is often used to analyze health-related data and trends within specific populations. This can include studying the prevalence of certain diseases or conditions, identifying disparities in healthcare access and outcomes, and evaluating the effectiveness of public health interventions. Demographic data can also be used to inform policy decisions and allocate resources to address population health needs.

The term "environment" in a medical context generally refers to the external conditions and surroundings that can have an impact on living organisms, including humans. This includes both physical factors such as air quality, water supply, soil composition, temperature, and radiation, as well as biological factors such as the presence of microorganisms, plants, and animals.

In public health and epidemiology, the term "environmental exposure" is often used to describe the contact between an individual and a potentially harmful environmental agent, such as air pollution or contaminated water. These exposures can have significant impacts on human health, contributing to a range of diseases and disorders, including respiratory illnesses, cancer, neurological disorders, and reproductive problems.

Efforts to protect and improve the environment are therefore critical for promoting human health and preventing disease. This includes measures to reduce pollution, conserve natural resources, promote sustainable development, and mitigate the impacts of climate change.

"World Health" is not a term that has a specific medical definition. However, it is often used in the context of global health, which can be defined as:

"The area of study, research and practice that places a priority on improving health and achieving equity in health for all people worldwide. It emphasizes trans-national health issues, determinants, and solutions; involves many disciplines within and beyond the health sciences and engages stakeholders from across sectors and societies." (World Health Organization)

Therefore, "world health" could refer to the overall health status and health challenges faced by populations around the world. It encompasses a broad range of factors that affect the health of individuals and communities, including social, economic, environmental, and political determinants. The World Health Organization (WHO) plays a key role in monitoring and promoting global health, setting international standards and guidelines, and coordinating responses to global health emergencies.

Population dynamics, in the context of public health and epidemiology, refers to the study of the changes in size and structure of a population over time, as well as the factors that contribute to those changes. This can include birth rates, death rates, migration patterns, aging, and other demographic characteristics. Understanding population dynamics is crucial for planning and implementing public health interventions, such as vaccination programs or disease prevention strategies, as they allow researchers and policymakers to identify vulnerable populations, predict future health trends, and evaluate the impact of public health initiatives.

HIV (Human Immunodeficiency Virus) infection is a viral illness that progressively attacks and weakens the immune system, making individuals more susceptible to other infections and diseases. The virus primarily infects CD4+ T cells, a type of white blood cell essential for fighting off infections. Over time, as the number of these immune cells declines, the body becomes increasingly vulnerable to opportunistic infections and cancers.

HIV infection has three stages:

1. Acute HIV infection: This is the initial stage that occurs within 2-4 weeks after exposure to the virus. During this period, individuals may experience flu-like symptoms such as fever, fatigue, rash, swollen glands, and muscle aches. The virus replicates rapidly, and the viral load in the body is very high.
2. Chronic HIV infection (Clinical latency): This stage follows the acute infection and can last several years if left untreated. Although individuals may not show any symptoms during this phase, the virus continues to replicate at low levels, and the immune system gradually weakens. The viral load remains relatively stable, but the number of CD4+ T cells declines over time.
3. AIDS (Acquired Immunodeficiency Syndrome): This is the most advanced stage of HIV infection, characterized by a severely damaged immune system and numerous opportunistic infections or cancers. At this stage, the CD4+ T cell count drops below 200 cells/mm3 of blood.

It's important to note that with proper antiretroviral therapy (ART), individuals with HIV infection can effectively manage the virus, maintain a healthy immune system, and significantly reduce the risk of transmission to others. Early diagnosis and treatment are crucial for improving long-term health outcomes and reducing the spread of HIV.

Air pollutants are substances or mixtures of substances present in the air that can have negative effects on human health, the environment, and climate. These pollutants can come from a variety of sources, including industrial processes, transportation, residential heating and cooking, agricultural activities, and natural events. Some common examples of air pollutants include particulate matter, nitrogen dioxide, sulfur dioxide, ozone, carbon monoxide, and volatile organic compounds (VOCs).

Air pollutants can cause a range of health effects, from respiratory irritation and coughing to more serious conditions such as bronchitis, asthma, and cancer. They can also contribute to climate change by reacting with other chemicals in the atmosphere to form harmful ground-level ozone and by directly absorbing or scattering sunlight, which can affect temperature and precipitation patterns.

Air quality standards and regulations have been established to limit the amount of air pollutants that can be released into the environment, and efforts are ongoing to reduce emissions and improve air quality worldwide.

I'm sorry for any confusion, but the term "Great Britain" is not a medical concept or condition. It is a geographical and political term referring to the largest island in the British Isles, on which the majority of England, Scotland, and Wales are located. It's also used to refer to the political union of these three countries, which is called the United Kingdom. Therefore, it doesn't have a medical definition.

Mortality, in medical terms, refers to the state or condition of being mortal; the quality or fact of being subject to death. It is often used in reference to the mortality rate, which is the number of deaths in a specific population, divided by the size of that population, per a given time period. This can be used as a measure of the risk of death among a population.

Neoplasms are abnormal growths of cells or tissues in the body that serve no physiological function. They can be benign (non-cancerous) or malignant (cancerous). Benign neoplasms are typically slow growing and do not spread to other parts of the body, while malignant neoplasms are aggressive, invasive, and can metastasize to distant sites.

Neoplasms occur when there is a dysregulation in the normal process of cell division and differentiation, leading to uncontrolled growth and accumulation of cells. This can result from genetic mutations or other factors such as viral infections, environmental exposures, or hormonal imbalances.

Neoplasms can develop in any organ or tissue of the body and can cause various symptoms depending on their size, location, and type. Treatment options for neoplasms include surgery, radiation therapy, chemotherapy, immunotherapy, and targeted therapy, among others.

I'm sorry for any confusion, but the term "Europe" is a geographical and political designation, rather than a medical one. It refers to the continent located entirely in the Northern Hemisphere and mostly in the Eastern Hemisphere. It is bordered by the Arctic Ocean to the north, the Atlantic Ocean to the west, and the Mediterranean Sea to the south. Europe is made up of approximately 50 countries, depending on how one defines a "country."

If you have any questions related to medical terminology or health-related topics, I'd be happy to help answer them!

"Forecasting" is not a term that has a specific medical definition. It is a general term used in various fields, including finance, economics, and meteorology, to describe the process of making predictions or estimates about future events or trends based on historical data, trends, and other relevant factors. In healthcare and public health, forecasting may be used to predict the spread of diseases, identify potential shortages of resources such as hospital beds or medical equipment, or plan for future health care needs. However, there is no medical definition for "forecasting" itself.

A diet, in medical terms, refers to the planned and regular consumption of food and drinks. It is a balanced selection of nutrient-rich foods that an individual eats on a daily or periodic basis to meet their energy needs and maintain good health. A well-balanced diet typically includes a variety of fruits, vegetables, whole grains, lean proteins, and low-fat dairy products.

A diet may also be prescribed for therapeutic purposes, such as in the management of certain medical conditions like diabetes, hypertension, or obesity. In these cases, a healthcare professional may recommend specific restrictions or modifications to an individual's regular diet to help manage their condition and improve their overall health.

It is important to note that a healthy and balanced diet should be tailored to an individual's age, gender, body size, activity level, and any underlying medical conditions. Consulting with a healthcare professional, such as a registered dietitian or nutritionist, can help ensure that an individual's dietary needs are being met in a safe and effective way.

Epidemiologic studies are investigations that seek to understand the distribution, patterns, and determinants of health and disease within a population. These studies aim to identify the frequency and occurrence of diseases or health-related events, as well as the factors that contribute to their occurrence. This information is used to develop public health policies and interventions to prevent or control diseases and promote overall health.

There are several types of epidemiologic studies, including:

1. Descriptive studies: These studies describe the characteristics of a population and the distribution of a disease or health-related event within that population. They do not typically investigate causes or risk factors.
2. Analytical studies: These studies examine the relationship between exposures (risk factors) and outcomes (diseases or health-related events). There are two main types of analytical studies: observational studies and experimental studies.
3. Observational studies: In these studies, researchers observe and collect data on a population without intervening or manipulating any variables. There are several types of observational studies, including cohort studies, case-control studies, and cross-sectional studies.
4. Cohort studies: These studies follow a group of people (a cohort) over time to see if they develop a particular disease or health-related event. Researchers collect data on exposures and outcomes at multiple points in time.
5. Case-control studies: These studies compare people with a specific disease or health-related event (cases) to people without the disease or event (controls). Researchers then look back in time to see if there are any differences in exposures between the two groups.
6. Cross-sectional studies: These studies collect data on exposures and outcomes at a single point in time. They are useful for estimating the prevalence of a disease or health-related event, but they cannot establish causality.
7. Experimental studies: In these studies, researchers manipulate variables to see if they have an effect on a particular outcome. The most common type of experimental study is a randomized controlled trial (RCT), in which participants are randomly assigned to receive either the intervention being tested or a control group.

Epidemiologic studies can provide valuable insights into the causes and consequences of diseases and health-related events, as well as potential interventions to prevent or treat them. However, they must be carefully designed and conducted to minimize bias and confounding, and their results should be interpreted with caution.

A meta-analysis is a statistical method used to combine and summarize the results of multiple independent studies, with the aim of increasing statistical power, improving estimates of effect size, and identifying sources of heterogeneity. It involves systematically searching for and selecting relevant studies, assessing their quality and risk of bias, extracting and analyzing data using appropriate statistical models, and interpreting the findings in the context of the existing literature. Meta-analyses can provide more reliable evidence than individual studies, especially when the results are inconsistent or inconclusive, and can inform clinical guidelines, public health policies, and future research directions.

Selection bias is a type of statistical bias that occurs when the sample used in a study is not representative of the population as a whole, typically because of the way the sample was selected or because some members of the intended sample were excluded. This can lead to skewed or inaccurate results, as the sample may not accurately reflect the characteristics and behaviors of the entire population.

Selection bias can occur in various ways, such as through self-selection (when individuals choose whether or not to participate in a study), through the use of nonrandom sampling methods (such as convenience sampling or snowball sampling), or through the exclusion of certain groups or individuals from the sample. This type of bias is particularly problematic in observational studies, as it can be difficult to control for all of the factors that may influence the results.

To minimize the risk of selection bias, researchers often use random sampling methods (such as simple random sampling or stratified random sampling) to ensure that the sample is representative of the population. They may also take steps to increase the diversity of the sample and to reduce the likelihood of self-selection. By carefully designing and implementing their studies, researchers can help to minimize the impact of selection bias on their results and improve the validity and reliability of their findings.

Multivariate analysis is a statistical method used to examine the relationship between multiple independent variables and a dependent variable. It allows for the simultaneous examination of the effects of two or more independent variables on an outcome, while controlling for the effects of other variables in the model. This technique can be used to identify patterns, associations, and interactions among multiple variables, and is commonly used in medical research to understand complex health outcomes and disease processes. Examples of multivariate analysis methods include multiple regression, factor analysis, cluster analysis, and discriminant analysis.

DNA Sequence Analysis is the systematic determination of the order of nucleotides in a DNA molecule. It is a critical component of modern molecular biology, genetics, and genetic engineering. The process involves determining the exact order of the four nucleotide bases - adenine (A), guanine (G), cytosine (C), and thymine (T) - in a DNA molecule or fragment. This information is used in various applications such as identifying gene mutations, studying evolutionary relationships, developing molecular markers for breeding, and diagnosing genetic diseases.

The process of DNA Sequence Analysis typically involves several steps, including DNA extraction, PCR amplification (if necessary), purification, sequencing reaction, and electrophoresis. The resulting data is then analyzed using specialized software to determine the exact sequence of nucleotides.

In recent years, high-throughput DNA sequencing technologies have revolutionized the field of genomics, enabling the rapid and cost-effective sequencing of entire genomes. This has led to an explosion of genomic data and new insights into the genetic basis of many diseases and traits.

An "ecosystem" is not a term that has a specific medical definition, as it is a concept that is more commonly used in the fields of ecology and environmental science. However, I can provide you with the general ecological definition of an ecosystem:

An ecosystem is a community of living organisms interacting with each other and their non-living environment, including both biotic factors (plants, animals, microorganisms) and abiotic factors (climate, soil, water, and air). These interactions create a complex network of relationships that form the foundation of ecological processes, such as energy flow, nutrient cycling, and population dynamics.

While there is no direct medical definition for an ecosystem, understanding the principles of ecosystems can have important implications for human health. For example, healthy ecosystems can provide clean air and water, regulate climate, support food production, and offer opportunities for recreation and relaxation, all of which contribute to overall well-being. Conversely, degraded ecosystems can lead to increased exposure to environmental hazards, reduced access to natural resources, and heightened risks of infectious diseases. Therefore, maintaining the health and integrity of ecosystems is crucial for promoting human health and preventing disease.

Radiometry is the measurement of electromagnetic radiation, including visible light. It quantifies the amount and characteristics of radiant energy in terms of power or intensity, wavelength, direction, and polarization. In medical physics, radiometry is often used to measure therapeutic and diagnostic radiation beams used in various imaging techniques and cancer treatments such as X-rays, gamma rays, and ultraviolet or infrared light. Radiometric measurements are essential for ensuring the safe and effective use of these medical technologies.

I apologize, but "Canada" is not a medical term or concept. It is a country located in North America, known for its vast wilderness, multicultural cities, and significant natural resources. If you have any questions related to healthcare or medical terms, I would be happy to help answer those!

Computer-assisted image processing is a medical term that refers to the use of computer systems and specialized software to improve, analyze, and interpret medical images obtained through various imaging techniques such as X-ray, CT (computed tomography), MRI (magnetic resonance imaging), ultrasound, and others.

The process typically involves several steps, including image acquisition, enhancement, segmentation, restoration, and analysis. Image processing algorithms can be used to enhance the quality of medical images by adjusting contrast, brightness, and sharpness, as well as removing noise and artifacts that may interfere with accurate diagnosis. Segmentation techniques can be used to isolate specific regions or structures of interest within an image, allowing for more detailed analysis.

Computer-assisted image processing has numerous applications in medical imaging, including detection and characterization of lesions, tumors, and other abnormalities; assessment of organ function and morphology; and guidance of interventional procedures such as biopsies and surgeries. By automating and standardizing image analysis tasks, computer-assisted image processing can help to improve diagnostic accuracy, efficiency, and consistency, while reducing the potential for human error.

In the context of medicine and medical devices, calibration refers to the process of checking, adjusting, or confirming the accuracy of a measurement instrument or system. This is typically done by comparing the measurements taken by the device being calibrated to those taken by a reference standard of known accuracy. The goal of calibration is to ensure that the medical device is providing accurate and reliable measurements, which is critical for making proper diagnoses and delivering effective treatment. Regular calibration is an important part of quality assurance and helps to maintain the overall performance and safety of medical devices.

The term "European Continental Ancestry Group" is a medical/ethnic classification that refers to individuals who trace their genetic ancestry to the continent of Europe. This group includes people from various ethnic backgrounds and nationalities, such as Northern, Southern, Eastern, and Western European descent. It is often used in research and medical settings for population studies or to identify genetic patterns and predispositions to certain diseases that may be more common in specific ancestral groups. However, it's important to note that this classification can oversimplify the complex genetic diversity within and between populations, and should be used with caution.

Inbreeding, in a medical context, refers to the practice of mating closely related individuals within a given family or breeding population. This leads to an increased proportion of homozygous genes, meaning that the same alleles (versions of a gene) are inherited from both parents. As a result, recessive traits and disorders become more likely to be expressed because the necessary dominant allele may be absent.

In human medicine, consanguinity is the term often used instead of inbreeding, and it refers to relationships between individuals who share a common ancestor. Consanguinity increases the risk of certain genetic disorders due to the increased likelihood of sharing harmful recessive genes. The closer the relationship, the higher the risk.

In animal breeding, inbreeding can lead to reduced fertility, lower birth weights, higher infant mortality, and a decreased lifespan. It is crucial to maintain genetic diversity within populations to ensure their overall health and vigor.

I apologize, but "Brazil" is not a medical term or condition. It is the largest country in both South America and Latin America by land area and population. If you have any questions related to medical terminology or health concerns, please provide more information and I will do my best to help.

In medical statistics, a statistical distribution refers to the pattern of frequency or proportion of certain variables in a population. It describes how the data points in a sample are distributed and can be used to make inferences about a larger population. There are various types of statistical distributions, including normal (or Gaussian) distribution, binomial distribution, Poisson distribution, and exponential distribution, among others. These distributions have specific mathematical properties that allow researchers to calculate probabilities and make predictions based on the data. For example, a normal distribution is characterized by its mean and standard deviation, while a Poisson distribution models the number of events occurring within a fixed interval of time or space. Understanding statistical distributions is crucial for interpreting medical research findings and making informed decisions in healthcare.

A phenotype is the physical or biochemical expression of an organism's genes, or the observable traits and characteristics resulting from the interaction of its genetic constitution (genotype) with environmental factors. These characteristics can include appearance, development, behavior, and resistance to disease, among others. Phenotypes can vary widely, even among individuals with identical genotypes, due to differences in environmental influences, gene expression, and genetic interactions.

Gene frequency, also known as allele frequency, is a measure in population genetics that reflects the proportion of a particular gene or allele (variant of a gene) in a given population. It is calculated as the number of copies of a specific allele divided by the total number of all alleles at that genetic locus in the population.

For example, if we consider a gene with two possible alleles, A and a, the gene frequency of allele A (denoted as p) can be calculated as follows:

p = (number of copies of allele A) / (total number of all alleles at that locus)

Similarly, the gene frequency of allele a (denoted as q) would be:

q = (number of copies of allele a) / (total number of all alleles at that locus)

Since there are only two possible alleles for this gene in this example, p + q = 1. These frequencies can help researchers understand genetic diversity and evolutionary processes within populations.

Weaning is the process of gradually introducing an infant or young child to a new source of nutrition, such as solid foods, while simultaneously decreasing their dependence on breast milk or formula. This process can begin when the child is developmentally ready, typically around 6 months of age, and involves offering them small amounts of pureed or mashed foods to start, then gradually introducing more textured and varied foods as they become comfortable with the new diet. The weaning process should be done slowly and under the guidance of a healthcare provider to ensure that the child's nutritional needs are being met and to avoid any potential digestive issues.

Computer-assisted image interpretation is the use of computer algorithms and software to assist healthcare professionals in analyzing and interpreting medical images. These systems use various techniques such as pattern recognition, machine learning, and artificial intelligence to help identify and highlight abnormalities or patterns within imaging data, such as X-rays, CT scans, MRI, and ultrasound images. The goal is to increase the accuracy, consistency, and efficiency of image interpretation, while also reducing the potential for human error. It's important to note that these systems are intended to assist healthcare professionals in their decision making process and not to replace them.

In medical terms, "fossils" do not have a specific or direct relevance to the field. However, in a broader scientific context, fossils are the remains or impressions of prehistoric organisms preserved in petrified form or as a mold or cast in rock. They offer valuable evidence about the Earth's history and the life forms that existed on it millions of years ago.

Paleopathology is a subfield of paleontology that deals with the study of diseases in fossils, which can provide insights into the evolution of diseases and human health over time.

Genetic markers are specific segments of DNA that are used in genetic mapping and genotyping to identify specific genetic locations, diseases, or traits. They can be composed of short tandem repeats (STRs), single nucleotide polymorphisms (SNPs), restriction fragment length polymorphisms (RFLPs), or variable number tandem repeats (VNTRs). These markers are useful in various fields such as genetic research, medical diagnostics, forensic science, and breeding programs. They can help to track inheritance patterns, identify genetic predispositions to diseases, and solve crimes by linking biological evidence to suspects or victims.

Medical mass screening, also known as population screening, is a public health service that aims to identify and detect asymptomatic individuals in a given population who have or are at risk of a specific disease. The goal is to provide early treatment, reduce morbidity and mortality, and prevent the spread of diseases within the community.

A mass screening program typically involves offering a simple, quick, and non-invasive test to a large number of people in a defined population, regardless of their risk factors or symptoms. Those who test positive are then referred for further diagnostic tests and appropriate medical interventions. Examples of mass screening programs include mammography for breast cancer detection, PSA (prostate-specific antigen) testing for prostate cancer, and fecal occult blood testing for colorectal cancer.

It is important to note that mass screening programs should be evidence-based, cost-effective, and ethically sound, with clear benefits outweighing potential harms. They should also consider factors such as the prevalence of the disease in the population, the accuracy and reliability of the screening test, and the availability and effectiveness of treatment options.

In the field of medical imaging, "phantoms" refer to physical objects that are specially designed and used for calibration, quality control, and evaluation of imaging systems. These phantoms contain materials with known properties, such as attenuation coefficients or spatial resolution, which allow for standardized measurement and comparison of imaging parameters across different machines and settings.

Imaging phantoms can take various forms depending on the modality of imaging. For example, in computed tomography (CT), a common type of phantom is the "water-equivalent phantom," which contains materials with similar X-ray attenuation properties as water. This allows for consistent measurement of CT dose and image quality. In magnetic resonance imaging (MRI), phantoms may contain materials with specific relaxation times or magnetic susceptibilities, enabling assessment of signal-to-noise ratio, spatial resolution, and other imaging parameters.

By using these standardized objects, healthcare professionals can ensure the accuracy, consistency, and reliability of medical images, ultimately contributing to improved patient care and safety.

Hospitalization is the process of admitting a patient to a hospital for the purpose of receiving medical treatment, surgery, or other health care services. It involves staying in the hospital as an inpatient, typically under the care of doctors, nurses, and other healthcare professionals. The length of stay can vary depending on the individual's medical condition and the type of treatment required. Hospitalization may be necessary for a variety of reasons, such as to receive intensive care, to undergo diagnostic tests or procedures, to recover from surgery, or to manage chronic illnesses or injuries.

An allele is a variant form of a gene that is located at a specific position on a specific chromosome. Alleles are alternative forms of the same gene that arise by mutation and are found at the same locus or position on homologous chromosomes.

Each person typically inherits two copies of each gene, one from each parent. If the two alleles are identical, a person is said to be homozygous for that trait. If the alleles are different, the person is heterozygous.

For example, the ABO blood group system has three alleles, A, B, and O, which determine a person's blood type. If a person inherits two A alleles, they will have type A blood; if they inherit one A and one B allele, they will have type AB blood; if they inherit two B alleles, they will have type B blood; and if they inherit two O alleles, they will have type O blood.

Alleles can also influence traits such as eye color, hair color, height, and other physical characteristics. Some alleles are dominant, meaning that only one copy of the allele is needed to express the trait, while others are recessive, meaning that two copies of the allele are needed to express the trait.

Reference values, also known as reference ranges or reference intervals, are the set of values that are considered normal or typical for a particular population or group of people. These values are often used in laboratory tests to help interpret test results and determine whether a patient's value falls within the expected range.

The process of establishing reference values typically involves measuring a particular biomarker or parameter in a large, healthy population and then calculating the mean and standard deviation of the measurements. Based on these statistics, a range is established that includes a certain percentage of the population (often 95%) and excludes extreme outliers.

It's important to note that reference values can vary depending on factors such as age, sex, race, and other demographic characteristics. Therefore, it's essential to use reference values that are specific to the relevant population when interpreting laboratory test results. Additionally, reference values may change over time due to advances in measurement technology or changes in the population being studied.

Species specificity is a term used in the field of biology, including medicine, to refer to the characteristic of a biological entity (such as a virus, bacterium, or other microorganism) that allows it to interact exclusively or preferentially with a particular species. This means that the biological entity has a strong affinity for, or is only able to infect, a specific host species.

For example, HIV is specifically adapted to infect human cells and does not typically infect other animal species. Similarly, some bacterial toxins are species-specific and can only affect certain types of animals or humans. This concept is important in understanding the transmission dynamics and host range of various pathogens, as well as in developing targeted therapies and vaccines.

Breast neoplasms refer to abnormal growths in the breast tissue that can be benign or malignant. Benign breast neoplasms are non-cancerous tumors or growths, while malignant breast neoplasms are cancerous tumors that can invade surrounding tissues and spread to other parts of the body.

Breast neoplasms can arise from different types of cells in the breast, including milk ducts, milk sacs (lobules), or connective tissue. The most common type of breast cancer is ductal carcinoma, which starts in the milk ducts and can spread to other parts of the breast and nearby structures.

Breast neoplasms are usually detected through screening methods such as mammography, ultrasound, or MRI, or through self-examination or clinical examination. Treatment options for breast neoplasms depend on several factors, including the type and stage of the tumor, the patient's age and overall health, and personal preferences. Treatment may include surgery, radiation therapy, chemotherapy, hormone therapy, or targeted therapy.

"California" is a geographical location and does not have a medical definition. It is a state located on the west coast of the United States, known for its diverse landscape including mountains, beaches, and forests. However, in some contexts, "California" may refer to certain medical conditions or situations that are associated with the state, such as:

* California encephalitis: a viral infection transmitted by mosquitoes that is common in California and other western states.
* California king snake: a non-venomous snake species found in California and other parts of the southwestern United States, which can bite and cause allergic reactions in some people.
* California roll: a type of sushi roll that originated in California and is made with avocado, cucumber, and crab meat, which may pose an allergy risk for some individuals.

It's important to note that these uses of "California" are not medical definitions per se, but rather descriptive terms that refer to specific conditions or situations associated with the state.

In the context of medicine and pharmacology, "kinetics" refers to the study of how a drug moves throughout the body, including its absorption, distribution, metabolism, and excretion (often abbreviated as ADME). This field is called "pharmacokinetics."

1. Absorption: This is the process of a drug moving from its site of administration into the bloodstream. Factors such as the route of administration (e.g., oral, intravenous, etc.), formulation, and individual physiological differences can affect absorption.

2. Distribution: Once a drug is in the bloodstream, it gets distributed throughout the body to various tissues and organs. This process is influenced by factors like blood flow, protein binding, and lipid solubility of the drug.

3. Metabolism: Drugs are often chemically modified in the body, typically in the liver, through processes known as metabolism. These changes can lead to the formation of active or inactive metabolites, which may then be further distributed, excreted, or undergo additional metabolic transformations.

4. Excretion: This is the process by which drugs and their metabolites are eliminated from the body, primarily through the kidneys (urine) and the liver (bile).

Understanding the kinetics of a drug is crucial for determining its optimal dosing regimen, potential interactions with other medications or foods, and any necessary adjustments for special populations like pediatric or geriatric patients, or those with impaired renal or hepatic function.

"Kaplan-Meier Survival Estimates". statsdirect.co.uk. Retrieved May 12, 2023. "Kaplan-Meier method in SPSS Statistics , Laerd ... Survival > Kaplan-Meier... menu. Julia: the Survival.jl package includes the Kaplan-Meier estimator. Survival Analysis ... Stalpers, Lukas J A; Kaplan, Edward L (May 4, 2018). "Edward L. Kaplan and the Kaplan-Meier Survival Curve". BSHM Bulletin: ... Accrual and The Kaplan-Meier Estimate". Cancer Guide. Statistics. Staub, Linda; Gekenidis, Alexandros (March 7, 2011). "Kaplan- ...
"Kaplan-Meier and Nelson-Aalen Estimators". 21 September 2008. "Kaplan-Meier Survival Estimates". Kysely, Jan; Picek, Jan; ... The Nelson-Aalen estimator is directly related to the Kaplan-Meier estimator and both maximize the empirical likelihood. " ... It is used in survival theory, reliability engineering and life insurance to estimate the cumulative number of expected events ... Beranova, Romana (2010). "Estimating extremes in climate change simulations using the peaks-over-threshold method with a non- ...
An early paper to use the Kaplan-Meier estimator for estimating censored costs was Quesenberry et al. (1989), however this ... 2012). "Techniques for estimating health care costs with censored data: an overview for the health services researcher". ... 1997). "Estimating medical costs from incomplete follow-up data". Biometrics. 53 (2): 419-434. doi:10.2307/2533947. JSTOR ... the maximum likelihood estimate (MLE) of λ {\displaystyle \lambda } , as follows: l ( λ ) = log ⁡ ( L ( λ ) ) = k log ⁡ ( λ ...
std.err is the standard error of the estimated survival. The standard error of the Kaplan-Meier product-limit estimate it is ... but it is usually estimated using the Kaplan-Meier (KM) curve. The graph shows the KM plot for the aml data and can be ... as determined using the Kaplan-Meier product-limit estimate. ... Kaplan-Meier curves and log-rank tests are most useful when the ... Survival analysis is used in several ways: To describe the survival times of members of a group Life tables Kaplan-Meier curves ...
The resolution of these endpoints are usually depicted using Kaplan-Meier survival curves. These curves relate the proportion ... Other HR models have different formulations and the interpretation of the parameter estimates differs accordingly. In its ... the magnitude of distance between the Kaplan-Meier plots. Hazard ratios do not reflect a time unit of the study. The difference ... as estimated by regression models that treat the log of the HR as a function of a baseline hazard h 0 ( t ) {\displaystyle h_{0 ...
A Kaplan-Meier analysis of the data estimated that about 50% of symptomatic people would die by the age of 25. More recent ...
... may refer to: Kaplan-Meier estimator,estimates the fraction of patients living for a certain amount of time after treatment ...
Dabrowska's estimator, from her paper "Kaplan-Meier estimate on the plane" (Annals of Statistics, 1988) is a widely used tool ...
In these situations, the most common method to model the survival function is the non-parametric Kaplan-Meier estimator. This ... as estimated using the exponential curve fit to the data. An alternative to graphing the probability that the failure time is ... Mathematics portal Failure rate Frequency of exceedance Kaplan-Meier estimator Mean time to failure Residence time (statistics ... counts are statistically sufficient to make non-parametric maximum likelihood and least squares estimates of survival functions ...
Empirical likelihood Kaplan-Meier estimator for censored processes Survival function Q-Q plot A modern introduction to ... The empirical distribution function is an estimate of the cumulative distribution function that generated the points in the ... Count data Distribution fitting Dvoretzky-Kiefer-Wolfowitz inequality Empirical probability Empirical process Estimating ...
The problem with measuring overall survival by using the Kaplan-Meier or actuarial survival methods is that the estimates ... It can be thought of as the kaplan-meier survivor function for a particular year, divided by the expected survival rate in that ... Lambert PC, Thompson JR, Weston CL, Dickman PW (2007). "Estimating and modeling the cure fraction in population-based cancer ... There are several software suites available to estimate relative survival rates. Regression modelling can be performed using ...
... an electric motor constant Kaplan-Meier estimator, a non-parametric statistic used to estimate the survival function Kelley- ...
Kaplan Meier and Cox proportional hazard), and analysis of complex survey data. The software is an open-source project with ... cross tabulations and stratification with estimates of odds ratios, risk ratios, and risk differences, logistic regression ( ... Kaplan Meier and Cox proportional hazard), and analysis of complex survey data. The "Visual Dashboard" module is a lighter- ... cross tabulations and stratification with estimates of odds ratios, risk ratios, and risk differences, logistic regression ( ...
... tests whether k treatments in randomized block designs have identical effects Empirical likelihood Kaplan-Meier: estimates the ... A histogram is a simple nonparametric estimate of a probability distribution. Kernel density estimation is another method to ... estimates the accuracy/sampling distribution of a statistic Cochran's Q: tests whether k treatments in randomized block designs ... estimate a probability distribution. Nonparametric regression and semiparametric regression methods have been developed based ...
Journal of Microscopy, 142:259-276, 1986 A.J. Baddeley and R.D. Gill, Kaplan-Meier estimators of interpoint distance ... Australian and New Zealand Journal of Statistics 42:283-322, 2000 A. Baddeley, Time-invariance estimating equations. Bernoulli ...
... together with millions of in-house generated immunohistochemically stained tissue sections images and Kaplan-Meier plots ... The Blood Protein section presents estimated plasma concentrations of the proteins detected in human blood from mass ...
Mathematics portal Kaplan-Meier estimator Hazard ratio Mantel, Nathan (1966). "Evaluation of survival data and two new rank ... The logrank test statistic compares estimates of the hazard functions of the two groups at each observed event time. It is ... The logrank test is based on the same assumptions as the Kaplan-Meier survival curve-namely, that censoring is unrelated to ... is the estimate of the hazard ratio, then log ⁡ λ ^ ≈ Z 4 / D {\displaystyle \log {\hat {\lambda }}\approx Z\,{\sqrt {4/D}}} . ...
... is the Kaplan-Meier estimator, and G {\displaystyle G} is the censoring distribution function. The Dvoretzky-Kiefer-Wolfowitz ... More precisely, there is the one-sided estimate Pr ( sup x ∈ R ( F n ( x ) − F ( x ) ) > ε ) ≤ e − 2 n ε 2 for every ε ≥ 1 2 n ... The Dvoretzky-Kiefer-Wolfowitz inequality is obtained for the Kaplan-Meier estimator which is a right-censored data analog of ... It also estimates the tail probability of the Kolmogorov-Smirnov statistic. The inequalities above follow from the case where F ...
ISBN 0-252-06156-X. Brooker, Russell; Kaplan, Fran. "The Rosenwald Schools: An Impressive Legacy of Black-Jewish Collaboration ... Federal Reserve Bank of Chicago, "The Impact of Rosenwald Schools on Black Achievement", September 2011 Meier, Allison C. ( ... doi:10.1086/662962 Aaronson, Daniel; Mazumder, Bhashkar; Sanders, Seth G.; Taylor, Evan J. (May 4, 2020). "Estimating the ...
... normal distribution Survival analysis Kaplan-Meier estimator, proportional hazards model, Weibull distribution Accounting fraud ... this is likely to be a high estimate, given the predominance of males in the technology industry. Then the probability of ...
JSTOR 2281868 Description: First description of the now ubiquitous Kaplan-Meier estimator of survival functions from data with ... Peirce and Jastrow use logistic regression to estimate subjective probabilities of subjects's judgments of the heavier of two ... Kaplan, EL and Meier, P Publication data: 1958, Journal of the American Statistical Association, volume 53, pages 457-481. ... Peirce and Jastrow use logistic regression to estimate subjective probabilities of subjects's judgments of the heavier of two ...
For time-to-event outcome data that may be censored, survival analysis (e.g., Kaplan-Meier estimators and Cox proportional ... estimates of effect may be biased if not adjusted for the covariates (which may be unmeasured and therefore impossible to ... "discrepancies beyond chance do occur and differences in estimated magnitude of treatment effect are very common" between ... "Empirical evidence of bias in treatment effect estimates in controlled trials with different interventions and outcomes: meta- ...
The comparison is usually made through the Kaplan-Meier estimator approach. The simplest cure rate model was published by ... ISBN 0-89838-555-5. Lambert PC, Thompson JR, Weston CL, Dickman PW (2007). "Estimating and modeling the cure fraction in ...
... redirects to k-means clustering K-means++ K-medians clustering K-medoids K-statistic Kalman filter Kaplan-Meier estimator Kappa ... software Stein's example Proof of Stein's example Stein's lemma Stein's unbiased risk estimate Steiner system Stemplot - see ... and residuals in statistics Errors-in-variables models An Essay towards solving a Problem in the Doctrine of Chances Estimating ... Generalized chi-squared distribution Generalized Dirichlet distribution Generalized entropy index Generalized estimating ...
Belarusian-born Israeli US middleweight and World Boxing Association champion super welterweight Louis Kaplan ("Kid Kaplan"), ... It is estimated that more than half died directly as a result of the Holocaust. Georgy Arbatov, Soviet politician, academic and ... born Sussele-Meier Davidoff), former tobacco manufacturer, known as "King of Cigars" Bernard Delfont, impresario Mikhail ... rabbi Fanny Kaplan, would-be assassin of Lenin Menachem Mendel Schneerson, Rebbe of the Chabad-Lubavitch branch of Hasidic ...
Estimates of how many died as a result of the war vary, ranging from 151,000 to more than 1 Million. The Iraq War was ... Martin Sixsmith, "Fanny Kaplan's Attempt to Kill Lenin" in Was Revolution Inevitable?: Turning Points of the Russian Revolution ... Schlesinger, Arthur Meier. The Cycles of American History, (Boston: Houghton Mifflin, 1986), p 141. OCLC 13455179 Lawrence ... Estimates of total deaths in the genocide vary greatly from 2,000 to 100,000 dead. The discovery of Gold in California resulted ...
"The investigation of the recurrence rate of cholesteatoma using Kaplan-Meier survival analysis". Otology & Neurotology. 29 (6 ... In one study, the number of new cases of cholesteatoma in Iowa was estimated in 1975-76 to be just under one new case per ...
An estimate of in the order of 1000 dive injuries per year occur in the United States and Canada. Many of these involve ... Meier, Matthew (5 November 2021). "Inner-Ear Barotrauma vs. DCS". xray-mag.com. X-Ray Magazine. Retrieved 26 July 2022. Marx, ... Kaplan, Joseph. Alcock, Joe (ed.). "Barotrauma Medication". emedicine.medscape.com. Retrieved 15 January 2017. Bentz, Brandon G ... Kaplan, Joseph. Alcock, Joe (ed.). "Barotrauma Workup: Laboratory Studies, Imaging Studies, Other Tests". emedicine.medscape. ...
According to Irving Kaplan, prior to the 7th century, the coastal areas frequented by the Persian migrants were inhabited by " ... A French visitor to this Sultanate, named Morice estimated that about a tenth of the population was Swahili-speaking Arabs and ... Meier, Prita. "Swahili Port Cities: The Architecture of Elsewhere." (Bloomington Indiana: Indiana University press, 2016) Pg. ... Shimaore is spoken on Mayotte, and has an estimated 136,500 total speakers. Shimwali is spoken on Mwali, and has about 28,700 ...
Meier, S.A. (1999). "Angel I". In Van der Toorn, Karel; Becking, Bob; Van der Horst, Pieter Willem (eds.). Dictionary of ... Kaplan, Aryeh (1997). Sefer Yetzirah: The Book of Creation in Theory and Practice (2nd Revised ed.). Weiser Books. p. 168. ISBN ... both estimated to date from between the 3rd and 2nd century BCE. In later Jewish tradition, he became identified as one of the ... malak". Meier 1999, p. 47. Grossman 2011, p. 52. Van Henten 1999, p. 81. Grossman 2011, p. 51. Yerushalmi Rosh Hashanah 1:2. ...
Health Care Evaluation Mechanisms - Kaplan-Meier Estimate PubMed MeSh Term *Overview. Overview. subject area of * Blocking ...
"Kaplan-Meier Survival Estimates". statsdirect.co.uk. Retrieved May 12, 2023. "Kaplan-Meier method in SPSS Statistics , Laerd ... Survival > Kaplan-Meier... menu. Julia: the Survival.jl package includes the Kaplan-Meier estimator. Survival Analysis ... Stalpers, Lukas J A; Kaplan, Edward L (May 4, 2018). "Edward L. Kaplan and the Kaplan-Meier Survival Curve". BSHM Bulletin: ... Accrual and The Kaplan-Meier Estimate". Cancer Guide. Statistics. Staub, Linda; Gekenidis, Alexandros (March 7, 2011). "Kaplan- ...
Kaplan-Meier estimates A: progression to critical disease (ICU/mortality); B: death alone. HR-Hazard ratio. Licensed under CC- ... A weighted pooled estimate of the proportion testing positive was calculated for studies with at least 10 patients. Limitations ...
Kaplan-Meier Estimate * Language Development * Male * Motor Skills / drug effects * Neuronal Ceroid-Lipofuscinoses / drug ...
Kaplan-Meier estimate. ‡ Based on a stratified Cox-Proportional-Hazards model. § Based on a stratified Log-rank test. The pre- ... Kaplan-Meier estimate.. ‡ Based on a stratified Cox-Proportional-Hazards model. Both hazard ratios are compared with the ... The Kaplan-Meier curves for PFS are shown in Figure 1.. Table 10: Efficacy Results per IRC in Patients with CLL -- ITT ... The Kaplan-Meier curve for PFS is shown in Figure 2. There was no statistically significant difference in overall response ...
We identified relatives and estimated sex and birth year adjusted hazard ratios, i.e., the rate of BPD-diagnoses in relatives ... Heritability was estimated at 46% (95% CI = 39-53), and the remaining variance was explained by individually unique ... Therefore, we performed a total-population study estimating the familial aggregation and heritability of clinically diagnosed ... and used structural equation modeling to estimate heritability. The familial association decreased along with genetic ...
Kaplan-Meier was used to estimate survival. Of 537 HIV-positive patients screened, 150 (27.9%) had AHD of which 109 were ... The estimated age-specific probabilities of each outcome can help SB clinicians estimate the expected proportion of patients ... We used linear mixed-model regression to estimate VLS proportion and negative binomial mixed-model regression to estimate the ... METHODS: We estimated asthma prevalence by professional group, and explored associations of self-reported asthma with job- ...
Kaplan-Meier Estimate * Kidney / physiopathology* * Kidney Function Tests * Male * Middle Aged * Predictive Value of Tests ... Background: The objective of this study was to examine the joint associations of estimated glomerular filtration rate (eGFR) ...
Kaplan-Meier Estimate, *Lymphocyte Activation, *Lymphocyte Subsets, *Lymphocytes, Tumor-Infiltrating, *Neoadjuvant Therapy, ...
The author included 11 studies (3724 patients with mean age of 66 years). Kaplan-Meier risk estimates were calculated. The ... However, current medical intervention alone was estimated at least 3 to 8 times more cost-effective. In conclusion, current ... in order to allow for a more precise estimate of 30-day outcomes." In response to CMS criticism of the inclusion of an ... with recent estimates overlapping those of operated patients in randomized trials. ...
c Kaplan-Meier Estimate. d log-rank test. e χ2-test.. ... The estimated background risk of major birth defects and ... In the U.S. general population, the estimated background risk of major birth defects and miscarriage in clinically recognized ... d Hazard ratio estimated by Cox regression stratified by clinical trial, intended paclitaxel schedule, number of positive nodes ... the survival rate was estimated to be 86.9% in the AC→TH arm and 79.4% in the AC→T arm. The final OS analysis results from ...
Kaplan-Meier estimate.. † Based on Global Composite Response score.. ‡ Responses in blood and skin must have persisted for at ... The Kaplan-Meier curve for PFS by Investigator is shown in Figure 1. The estimated median follow-up for investigator-assessed ... The estimated background risk of major birth defects and miscarriage for the indicated population is unknown. All pregnancies ... By independent review committee assessment, the estimated median PFS was 6.7 months (95% CI, 5.6 to 9.4) in the POTELIGEO arm ...
Kaplan-Meier Estimates. Unrelated deaths occurring in the absence of progression were counted as events (progression) in this ... Kaplan-Meier Estimates. Unrelated deaths occurring in the absence of progression were counted as events (progression) in this ... Dosing formulas incorporating estimates of GFR (see DOSAGE and ADMINISTRATION) to provide predictable carboplatin injection ... Because renal function is often decreased in elderly patients, formula dosing of carboplatin injection based on estimates of ...
Kaplan-Meier estimates of survival by: a first surgery (total RS, partial RS, NOS RS, and biopsy), b treatment including or ... Survival was estimated by the Kaplan-Meier method and defined as the time from first surgery (corresponding to the histological ... MS were estimated for each group, and we showed that prolonging the adjuvant temozolomide beyond 6 cycles positively impacted ... Univariate Cox regression model was used to estimate hazard ratios (HRs) of strata versus reference level and their 95% Wald ...
A) Kaplan-Meier estimate for all cases; B)... * Figure 4. Estimates for time to TB disease diagnosis not attributed to recent ... A) Kaplan-Meier estimate for all... Tables. * Table 1. Median time to diagnosis of tuberculosis disease not attributed to ... Kaplan-Meier estimate for time to TB disease diagnosis not attributed to recent transmission among non-US-born persons after ... Kaplan-Meier estimate for time to TB disease diagnosis not attributed to recent transmission among non-US-born persons after ...
The Kaplan Meier estimates suggest that these two events are more closely linked in Spain than in Britain. However, we might ... A straightforward way to do this is to use Kaplan Meier survival estimates (see Tuma and Huinink 1990 for application of the ... The Kaplan Meier estimates corroborate the evidence presented above, and illustrate distinctive British and Spanish patterns. ... We then use simple event history analysis techniques, based on Kaplan Meier estimates, to identify the main differences in ...
Kaplan-Meier estimates of progression-free survival (PFS) by mRECIST.. 4. Discussion. In this study, we investigated the ... The Kaplan-Meier method was used for survival analysis to plot survival curves, and log-rank test was used for comparison of ... GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries," CA: a Cancer Journal for Clinicians, ...
Event rates based on Kaplan-Meier estimates at 3 years.. 1Excluding those of the vertebrae (cervical, thoracic, and lumbar), ... it is not always possible to reliably estimate their frequency or establish a causal relationship to drug exposure. ...
Kaplan-Meier Estimate. Williams JR, Trias E, Beilby PR, Lopez NI, Labut EM, C Bradford S, Roberts BR, McAllum EJ, Crouch PJ, ...
Estimate Expected Survival with Incomplete Data The following right-censored data produces a Kaplan-Meier estimate that never ... In order to estimate the expected lifetime, you must choose a model for the remaining probability in the tail. This can easily ...
The proportion of children found to be infected by age 6 months in each treatment group was estimated by using the Kaplan-Meier ... The estimated HIV transmission risk for the ZDV and placebo groups were 9.2% (95% confidence interval {CI}=5.0%-13.5%) and 18.6 ... Estimating the timing of mother-to-child transmission of human immunodeficiency virus in a breast-feeding population in ... The null hypothesis of no treatment effect was tested by using a normally distributed Z statistic computed from these estimates ...
Risk factors for RP recurrence were analysed using a Cox proportional hazards model, and Kaplan-Meier survival curves were ... Risk factors for RP recurrence were analysed using a Cox proportional hazards model, and Kaplan-Meier survival curves were ... Hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated using the Cox proportional hazards model. If no relapse ... Survival curves for treatment were drawn via the Kaplan-Meier method. Univariate analysis was performed to explore the risk ...
To address this issue we run a series of Kaplan-Meier survival estimates. ... The Kaplan-Meier survival estimate is commonly used to analyze time to event data and to compare two groups of subjects. The ... the Kaplan-Meier survival estimate and the Cox regression hazard model. The aim of these latest analyses is to check the ... Zakirova, D.F., Panteleev, D.S. and Zakirova, E.F. (2018), "Estimating bankruptcy probability of credit organizations", ...
Kaplan-Meier (K-M) estimate is one of the best options to be used to measure the fraction of subjects (users) without an event ... Goel, M. K., Khanna, P., & Kishore, J. (2010). Understanding survival analysis: Kaplan-Meier estimate. International Journal of ... Figure 1. Kaplan-Meier curve of time spent on page.. Figure 2. Fraction of users remaining as a function of time spent on page ... Next, we wanted to see whether there were differences in the time spent on page Kaplan-Meier curves when stratifying by various ...
These functions were estimated using Kaplan-Meier proposed methodology. It should be noted that no data were presented censored ... Median of these times were calculated according to the Kaplan Meier method and compared using the test of equality between ... the total number of patients in each group was estimated at 220. At the end of the fourth year a preliminary analysis of the ...
The Kaplan-Meier method was used to estimate PFS and OS. The log-rank test was used to test the differences between groups that ... RIP140 is a prognosis marker in colon cancer. (A) Kaplan-Meier plots of cumulative survival of Apc+/+, ApcΔ14/+, and ApcΔ14/+ ... B) Kaplan-Meier plots of OS of patients with tumors exhibiting low or high RIP140 mRNA expression. (C) RIP140 immunochemistry ... Kaplan-Meier plot of the cumulative survival of patients with low or high RIP140 gene expression. A log-rank test was used for ...
The observed risks were obtained by using Kaplan-Meier estimates evaluated at two years. ... We used Coxs proportional hazards models to estimate the coefficients for each risk factor using robust variance estimates to ... If she also has had anaemia in the past year her estimated risk is 1.4%. If she also has abdominal distension her estimated ... A 55 year old woman with a family history of ovarian cancer and consulting with loss of appetite has a 2.6% estimated risk of ...
Coverage was calculated using an enhanced estimation strategy that resembles the Kaplan-Meier estimation procedure [12]. The ... NIFS estimates were higher than estimates from the BRFSS and the National Health Interview Survey, suggesting NIFS estimates ... Estimates for children are based on data from the NIS-Flu, while estimates for adults are based on data from the NIFS. NIS-Flu ... To produce a national estimate of flu vaccination coverage for all persons 6 months and older, the estimates from the NIS-Flu ...
A) Kaplan-Meier estimates of PFS among patients evaluated for cytogenetic risk. High-risk patients had any of t(4;14), t(14;16 ... B) Kaplan-Meier estimates of PFS among patients in the ITT population. MRD-negative status was evaluated at a sensitivity ... Kaplan-Meier estimates of PFS. PFS: progression-free survival; ITT: intent-to-treat; D-Vd: daratumumab plus bortezomib and ...
Median follow-up was calculated using the reverse Kaplan-Meier method[20]. Survival functions were estimated by the Kaplan- ... Figure 1 Kaplan-Meier estimates of overall survival for advanced esophageal cancer patients not receiving chemotherapy with and ... Kaplan-Meier estimates of OS were calculated. Cox proportional hazards regression models were employed to examine factors ... Kaplan-Meier estimates of OS were calculated. Cox proportional hazards regression models were employed to examine factors ...
  • In other fields, Kaplan-Meier estimators may be used to measure the length of time people remain unemployed after a job loss, the time-to-failure of machine parts, or how long fleshy fruits remain on plants before they are removed by frugivores. (wikipedia.org)
  • The time-to-diagnostic test was estimated using Kaplan-Meier estimators. (scirp.org)
  • This report provides early estimates for the 2017-18 flu season of the percentage of people (children and adults) in the United States who had reported receiving a flu vaccination. (cdc.gov)
  • The final 2017-18 flu season vaccination coverage estimates will be available on the CDC FluVaxView webpage in September 2018. (cdc.gov)
  • 2 ) The Centers for Disease Control and Prevention (CDC) analyzed data from the Behavioral Risk Factor Surveillance System (BRFSS) to estimate flu vaccination coverage for the U.S. population of adults aged ≥18 years during the 2017-18 flu season. (cdc.gov)
  • For this report, CDC analyzed data from BRFSS for adults aged ≥18 years to estimate flu vaccination coverage from the 2017-18 flu season. (cdc.gov)
  • The 2017-18 flu season estimates were compared with 2016-17 flu season estimates. (cdc.gov)
  • Risk factors for RP recurrence were analysed using a Cox proportional hazards model, and Kaplan-Meier survival curves were drawn. (springer.com)
  • Cox proportional hazards regression analyses estimated the CVD hazard ratio associated with 9/11/2001 arrival in responders with and without dust cloud exposure, compared with arrival on or after 9/12/2001. (cdc.gov)
  • The following right-censored data produces a Kaplan-Meier estimate that never reaches zero because the last observation is censored. (wolfram.com)
  • We identified relatives and estimated sex and birth year adjusted hazard ratios, i.e., the rate of BPD-diagnoses in relatives to individuals with BPD-diagnosis compared to individuals with unaffected relatives, and used structural equation modeling to estimate heritability. (nature.com)
  • Preliminary estimates from other data sources do not show decreases in flu vaccination coverage. (cdc.gov)
  • 3 ) BRFSS data were collected from September 2017 through June 2018 from all 50 states to estimate vaccination coverage for vaccines administered from July 2017 through May 2018. (cdc.gov)
  • Estimates for the District of Columbia represent vaccination coverage through November 2017 based on interviews conducted September through December 2017. (cdc.gov)
  • Flu vaccination coverage estimates were calculated using Kaplan-Meier survival analysis to determine the cumulative flu vaccination coverage (≥1 dose) during July 2017 through May 2018 using monthly interview data collected September 2017 through June 2018. (cdc.gov)
  • The Kaplan-Meier estimator, also known as the product limit estimator, is a non-parametric statistic used to estimate the survival function from lifetime data. (wikipedia.org)
  • Estimated mean survival using four different parametric tail models. (wolfram.com)
  • A weighted pooled estimate of the proportion testing positive was calculated for studies with at least 10 patients. (cdc.gov)
  • The differences in survival between the patients in particular stages of both classifications were estimated with the log-rank test. (medscimonit.com)
  • At 6 months, 92.6% of patients (Kaplan-Meier estimate) were free from major system and/or procedure-related major complications, such as hospitalization, system revision, or death. (medscape.com)
  • The null hypothesis of no treatment effect was tested by using a normally distributed Z statistic computed from these estimates. (cdc.gov)
  • Kaplan-Meier estimates of CVD incidence used the generalized Wilcoxon test statistic to account for censored data. (cdc.gov)
  • The cumulative survival rates were calculated using the Kaplan-Meier method. (medscimonit.com)
  • An important advantage of the Kaplan-Meier curve is that the method can take into account some types of censored data, particularly right-censoring, which occurs if a patient withdraws from a study, is lost to follow-up, or is alive without event occurrence at last follow-up. (wikipedia.org)
  • The challenge is to estimate S ( t ) {\displaystyle S(t)} given this data. (wikipedia.org)
  • estimating the exposed-to-risk with aggregate data. (southampton.ac.uk)
  • The proportion of children found to be infected by age 6 months in each treatment group was estimated by using the Kaplan-Meier method. (cdc.gov)
  • We estimated time to first treatment interruption, time to restarting after interruption, and time to second interruption. (researchgate.net)
  • In order to estimate the expected lifetime, you must choose a model for the remaining probability in the tail. (wolfram.com)
  • Objective To derive and validate an algorithm to estimate the absolute risk of having ovarian cancer in women with and without symptoms. (bmj.com)
  • Information on high-risk conditions was missing for 1.2% of adults and were not included in the estimates by risk condition. (cdc.gov)
  • Overall survival was estimated with Kaplan-Meier analysis. (aats.org)
  • All estimates were weighted to the U.S. adult population with analysis conducted using SAS and SUDAAN statistical software to account for the complex survey design. (cdc.gov)
  • The objective of this study was to examine the joint associations of estimated glomerular filtration rate (eGFR) and urinary albumin excretion with incident stroke in a large national cohort study. (nih.gov)
  • Here, we show two derivations of the Kaplan-Meier estimator. (wikipedia.org)
  • Family and twin studies of Borderline Personality Disorder (BPD) have found familial aggregation and genetic propensity for BPD, but estimates vary widely. (nature.com)
  • Therefore, we performed a total-population study estimating the familial aggregation and heritability of clinically diagnosed BPD. (nature.com)
  • Estimates for children for the 2017-18 season have been published. (cdc.gov)
  • The Kaplan-Meier estimator, also known as the product limit estimator, is a non-parametric statistic used to estimate the survival function from lifetime data. (wikipedia.org)
  • The estimator is named after Edward L. Kaplan and Paul Meier, who each submitted similar manuscripts to the Journal of the American Statistical Association. (wikipedia.org)
  • A plot of the Kaplan-Meier estimator is a series of declining horizontal steps which, with a large enough sample size, approaches the true survival function for that population. (wikipedia.org)
  • Here, we show two derivations of the Kaplan-Meier estimator. (wikipedia.org)
  • The survival probability was estimated using the Kaplan-Meier estimator. (who.int)
  • This is the peer reviewed version of the following article: Cox, T. F., and Czanner, G. ( 2016) A practical divergence measure for survival distributions that can be estimated from Kaplan-Meier curves. (ljmu.ac.uk)
  • Utilizing Kaplan-Meier estimate curves, time to response periods involving the 2 groups showed no substantial distinction [Log-Rank P-value = 0.088] even though time for you to PLD Inhibitor list Remission was drastically Iranian J Psychiatry 16: 1, January 2021 ijps.tums.ac.irGemfibrozil in Remedy of Important Depressive Disorder distinctive amongst the two trial arms [Log-Rank p worth = 0.003]. (pdgfr.com)
  • Actuarial survival analysis used Kaplan-Meier curves and the log-rank test for comparison. (revespcardiol.org)
  • When no truncation or censoring occurs, the Kaplan-Meier curve is the complement of the empirical distribution function. (wikipedia.org)
  • The loss-adjusted survival results are that those lost to follow-up in specific prognostic compared with the crude actuarial estimate to stratum have the same probability of death as others demonstrate the magnitude of bias. (who.int)
  • Cox regression analysis was also used to estimate the effect of factors on the prediction of survival. (spandidos-publications.com)
  • Cox proportional hazards regression analyses estimated the CVD hazard ratio associated with 9/11/2001 arrival in responders with and without dust cloud exposure, compared with arrival on or after 9/12/2001. (cdc.gov)
  • Crude abatacept retention rates over 2 years were estimated using Kaplan-Meier analyses in biologic-naive and -failure patients. (springer.com)
  • Outcome measurements and statistical analysis: We performed parametric extrapolations to estimate overall and progression-free survival beyond the clinical trial period. (eur.nl)
  • In other fields, Kaplan-Meier estimators may be used to measure the length of time people remain unemployed after a job loss, the time-to-failure of machine parts, or how long fleshy fruits remain on plants before they are removed by frugivores. (wikipedia.org)
  • Cancer survival is the main indicator of outcome of who have a potential follow-up shorter than the time cancer health services or treatment, and an of the maximum estimated survival are 'censored' important component in maintaining cancer control cases. (who.int)
  • This chapter presents formulae that methodologically adjust for losses, and gives examples describing magnitude of bias in survival estimates without such adjustment. (who.int)
  • A total of 336 hospital series of treated new breast cancer cases from Mumbai with 24% lost to follow-up revealed a substantial bias of 7 per cent units for 3-year survival estimated with (54%) and without (61%) loss-adjustment. (who.int)
  • 2] or Kaplan-Meier [3] methods. (who.int)
  • Swedish costs and utility weights were used to estimate total costs, qualityadjusted life years (QALYs), and incremental cost-effectiveness ratios (ICERs). (eur.nl)
  • The relationship between insurance status and 1-year outcomes, including all-cause death, stroke recurrence and disability, was analysed using the shared frailty model in the Cox model or generalised estimating equation with consideration of the hospital's cluster effect. (bmj.com)
  • In particular, we model source contribution using a Dirichlet process as a prior for source profiles, which allows us to estimate the number of components that contribute to particle concentration rather than fixing this number beforehand. (imperial.ac.uk)
  • Hence, this correlation, explained by independent of the length of follow-up of an information on prognostic factors, can be utilized to individual patient, so that even recently diagnosed correct survival estimates. (who.int)