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The validity of clinical diagnoses is a fundamental topic in clinical psychology, because now there are some political administrations, as the IOM or the U.K. government, which are focusing on best evidence-based practice in clinical psychology. The most problematic issue in clinical psychology is to avoid wrong diagnoses which can have negative consequences on individual life and on the utility of clinical treatments. In the case of diagnoses based on self-report tests, the diagnostic decision about individual health is based on the comparison between its score and the cutoff, according to the frequentist approach to probability. However, the frequentist approach underestimates the possible risks of incorrect diagnoses based on cutoffs only. The Bayesian approach is a valid alternative to make diagnoses on the basis of the scores from psychological tests. The Bayes' theorem estimates the posterior probability of the presence of a pathology on the basis of the knowledge about the diffusion of this pathology (prior probability) and of the knowledge of sensitivity and specificity values of the test. With all this information, it is possible to estimate the diagnostic accuracy of some self-report tests used for assessing depression. We analyzed the diagnostic accuracy of the most used psychological tests of depression (Zung's Self-Rating Depression Scale, Hamilton Rating Scale for Depression, Center for Epidemiological Studies for Depression and the Beck Depression Inventory), together with a new scale (Teate Depression Inventory) developed with the IRT procedure, by analyzing the published works in which data about sensitivity and specificity of these scales are reported. Except the TDI, none of these scales can reach a satisfactory level of diagnostic accuracy, probably for the absence of an optimal procedure to select test items and subjects with clearly defined pathological symptoms which could allow the reduction of false positives in test scoring.

Proposals for the application of Bayes' Theorem as an aid to child abuse decision making are discussed critically. Subsequently, two examples of the application of research findings concerning signs of child abuse to decision making are demonstrated, using data from research studies of signs of physical abuse and sexual abuse. The calculation of the probability of the presence of abuse using Bayes' Theorem is described, given prevalence information and specific indicators of abuse. In addition, a method for describing the degree of imprecision in estimates of the probability of abuse is discussed. Specific issues that affect the valid application of research findings within Bayes' Theorem are discussed, including estimates of the prevalence or base rate of child abuse, sensitivity and specificity of diagnostic signs, and the independence of information that is combined in Bayes' Theorem. Recommendations for addressing these issues when Bayes' Theorem is applied to child abuse decision making are discussed, including assessment of the independence of indicators that are combined in Bayes' Theorem, suitable bases for estimating the prevalence of abuse, and the calculation of imprecision in probability estimates of abuse.

ObjectivesTo generate, via application of Bayes Theorem, accurate estimates about the size of Hispanic populations in California cities from very limited data on the surnames of those living in the cities.MethodsWe make use here of the ratio of those with the name "GARCIA" to those with the name "ANDERSON" in those cities, one of which is far more likely to be Hispanic and one of which is far more likely to be non‐Hispanic.ResultsFor four cities that vary dramatically in their Hispanic populations, using only two common names we are able to estimate the Hispanic population in the cities.ConclusionsWe lay the background for our surprising results by underscoring common fallacies in using surnames for purposes of ethnic identification, such as the belief that the proportion of bearers of a given name who are Hispanic can be specified as a unique percentage. We show that how "Hispanic" any given name will turn out to be is a function of the overall demography of the subpopulation being analyzed, which will also affect the distribution of names within that subpopulation.

Groups such as races, sexes, ethnic groups, & age classes are shown to be relevant to problems of selection for school & employment. Here, a Bayesian approach to problems of selection is developed & illustrated. The mean & standard deviation of the distribution of abilities in the candidate's group (race, sex, age, etc) constitute prior information; additional information is provided by the candidate's test scores. Combination of this information with the aid of Bayes's theorem provides a posterior estimate. It is shown that group membership is normally relevant to selection, implying a fundamental conflict between nondiscrimination (not using group membership for selection) & merit selection. Evaluation of the circumstances in which use of group membership is relevant to selection, reveals evidence that groups do differ in various attributes relevant to vocational success, including intelligence, literacy, personality, & criminality. 1 Figure, 102 References. Adapted from the source document.

Shallot plants or in Latin Alliumascalonicum L is a horticultural plant that gets a lot of attention from both the public and the government. Over the past few years shallots are included in the top six vegetable commodities that are exported together with cabbage, cabbage or flower cabbage, chili, tomatoes, potatoes. Onion plants are very susceptible to disease that can strike at any time. Some diseases that can attack the onion plant include Onion Caterpillar Disease, Leaf Snorer Disease, Trip, Moler Disease, Trotol, Dew Feather and Anthracnose. In this study using the Bayes theorem expert system, each symptom has a density value to the disease which is then calculated using the formula of the Bayes Theorem Method and processed using certain rules and inference using Forword Chaining. This research is expected to help the onion farmers in diagnosing the disease so that it can reduce the material losses with less than 60%.

Background: Non-fatal outcomes of disease and injury increasingly detract from the ability of the world's population to live in full health, a trend largely attributable to an epidemiological transition in many countries from causes affecting children, to non-communicable diseases (NCDs) more common in adults. For the Global Burden of Diseases, Injuries, and Risk Factors Study 2015 (GBD 2015), we estimated the incidence, prevalence, and years lived with disability for diseases and injuries at the global, regional, and national scale over the period of 1990 to 2015. Methods: We estimated incidence and prevalence by age, sex, cause, year, and geography with a wide range of updated and standardised analytical procedures. Improvements from GBD 2013 included the addition of new data sources, updates to literature reviews for 85 causes, and the identification and inclusion of additional studies published up to November, 2015, to expand the database used for estimation of non-fatal outcomes to 60 900 unique data sources. Prevalence and incidence by cause and sequelae were determined with DisMod-MR 2.1, an improved version of the DisMod-MR Bayesian meta-regression tool first developed for GBD 2010 and GBD 2013. For some causes, we used alternative modelling strategies where the complexity of the disease was not suited to DisMod-MR 2.1 or where incidence and prevalence needed to be determined from other data. For GBD 2015 we created a summary indicator that combines measures of income per capita, educational attainment, and fertility (the Socio-demographic Index [SDI]) and used it to compare observed patterns of health loss to the expected pattern for countries or locations with similar SDI scores. Findings: We generated 9·3 billion estimates from the various combinations of prevalence, incidence, and YLDs for causes, sequelae, and impairments by age, sex, geography, and year. In 2015, two causes had acute incidences in excess of 1 billion: upper respiratory infections (17·2 billion, 95% uncertainty interval [UI] 15·4-19·2 billion) and diarrhoeal diseases (2·39 billion, 2·30-2·50 billion). Eight causes of chronic disease and injury each affected more than 10% of the world's population in 2015: permanent caries, tension-type headache, iron-deficiency anaemia, age-related and other hearing loss, migraine, genital herpes, refraction and accommodation disorders, and ascariasis. The impairment that affected the greatest number of people in 2015 was anaemia, with 2·36 billion (2·35-2·37 billion) individuals affected. The second and third leading impairments by number of individuals affected were hearing loss and vision loss, respectively. Between 2005 and 2015, there was little change in the leading causes of years lived with disability (YLDs) on a global basis. NCDs accounted for 18 of the leading 20 causes of age-standardised YLDs on a global scale. Where rates were decreasing, the rate of decrease for YLDs was slower than that of years of life lost (YLLs) for nearly every cause included in our analysis. For low SDI geographies, Group 1 causes typically accounted for 20-30% of total disability, largely attributable to nutritional deficiencies, malaria, neglected tropical diseases, HIV/AIDS, and tuberculosis. Lower back and neck pain was the leading global cause of disability in 2015 in most countries. The leading cause was sense organ disorders in 22 countries in Asia and Africa and one in central Latin America; diabetes in four countries in Oceania; HIV/AIDS in three southern sub-Saharan African countries; collective violence and legal intervention in two north African and Middle Eastern countries; iron-deficiency anaemia in Somalia and Venezuela; depression in Uganda; onchoceriasis in Liberia; and other neglected tropical diseases in the Democratic Republic of the Congo. Interpretation: Ageing of the world's population is increasing the number of people living with sequelae of diseases and injuries. Shifts in the epidemiological profile driven by socioeconomic change also contribute to the continued increase in years lived with disability (YLDs) as well as the rate of increase in YLDs. Despite limitations imposed by gaps in data availability and the variable quality of the data available, the standardised and comprehensive approach of the GBD study provides opportunities to examine broad trends, compare those trends between countries or subnational geographies, benchmark against locations at similar stages of development, and gauge the strength or weakness of the estimates available.Bill & Melinda Gates Foundation. ; Theo Vos, Christine Allen, Megha Arora … Azmeraw T Amare . Ratilal Lalloo … Yohannes Adama Melaku … et al. (GBD 2015 Disease and Injury Incidence and Prevalence Collaborators)

El presente trabajo tiene como objetivo demostrar las causas sobre la migración de ecuatorianos hacia otros países, así también dar a conocer entre el índice migratorio cual es el rango de edad que migran con más exceso. El problema radica que Ecuador es uno de los países que tiene una gran cantidad de migrantes, frente a estas circunstancias se presenta características como: económicas, políticas, ofertas de trabajo, problemas familiares entre otros, siendo estos los motivos principales por cuales migran. Mediante el método de aplicación de Teorema de Bayes determinara la probabilidad exacta de cada una de las causas que afectan a los migrantes. Del total de 1'048.575 encuestados nos da un análisis del 34.09% deciden migrar a los estados unidos por desempleo, con la aplicación del teorema de Bayes en RStudio se pudo determinar la probabilidad de cada una de las causas por lo que la población migra a otros países. ; The present work aims to demonstrate the causes of the migration of Ecuadorians to other countries, as well as to make known among the migratory index what age range they migrate with the most excess. The problem is that Ecuador is one of the countries that have a large number of migrants, in the face of these circumstances there are characteristics such as economic, political, job offers, family problems among others, these being the main reasons why which migrate Using the Bayes Theorem application method, you will determine the exact probability of each of the causes that affect migrants. Of the total of 1,048,575 respondents gives us an analysis of 34.09% decide to migrate to the United States for unemployment, with the application of the Bayes theorem in RStudio it was possible to determine the probability of each of the causes by which the population migrates to other countries.

El presente trabajo tiene como objetivo demostrar las causas sobre la migración de ecuatorianos hacia otros países, así también dar a conocer entre el índice migratorio cual es el rango de edad que migran con más exceso. El problema radica que Ecuador es uno de los países que tiene una gran cantidad de migrantes, frente a estas circunstancias se presenta características como: económicas, políticas, ofertas de trabajo, problemas familiares entre otros, siendo estos los motivos principales por cuales migran. Mediante el método de aplicación de Teorema de Bayes determinara la probabilidad exacta de cada una de las causas que afectan a los migrantes. Del total de 1'048.575 encuestados nos da un análisis del 34.09% deciden migrar a los estados unidos por desempleo, con la aplicación del teorema de Bayes en RStudio se pudo determinar la probabilidad de cada una de las causas por lo que la población migra a otros países. ; The present work aims to demonstrate the causes of the migration of Ecuadorians to other countries, as well as to make known among the migratory index what age range they migrate with the most excess. The problem is that Ecuador is one of the countries that have a large number of migrants, in the face of these circumstances there are characteristics such as economic, political, job offers, family problems among others, these being the main reasons why which migrate Using the Bayes Theorem application method, you will determine the exact probability of each of the causes that affect migrants. Of the total of 1,048,575 respondents gives us an analysis of 34.09% decide to migrate to the United States for unemployment, with the application of the Bayes theorem in RStudio it was possible to determine the probability of each of the causes by which the population migrates to other countries.