Download ABC en emergencias by Alberto J. Machado PDF

By Alberto J. Machado

ABC en emergencias fue pensado como una herramienta de trabajo. Un ayuda memoria, o una guía rápida acerca de cómo enfrentamos, y que puntos relevantes no dejar de considerar, en distintas situaciones frecuentes en Emergencias.

Emergencias es l. a. especialidad médica que estudia los hechos y situaciones patológicas de aparición súbita que ponen en riesgo o comprometen los angeles salud.

En Emergencias el tiempo nos corre, y el momento en que tomamos diferentes conductas, durante los angeles evolución del paciente, inftuye directamente en el pronóstico.

En ninguna otra especialidad, se pone en juego l. a. capacidad de decisión tiempo dependiente.

La acción organizada y sistematizada frente a una emergencia, es l. a. que redundará en mayores beneficios, permitiéndonos valorar que "es" y que "no es" de riesgo important. Un manejo desorganizado, transforma los angeles situación, que de por sí ya es crítica, en un caos con el perjuicio directo sobre el paciente.

ABC en emergencias es un instructional de consulta rápida, cuando tenemos poco tiempo para consultar.

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1) is as- OPTIMAL ADMINISTRATION OF MULTIPLE SCREENING TESTS 51 sumed, m L Kil for R(id2···i=), 1=1 m '~ " K i,I Pi'I for R[i'12 i' .. i'rn ], 1=1 where Pi'I is the probability that the result of R is still unknown after knowing the outcomes of tests i~, i 2, ... ,i:_ 1 . When the parameters in our model are unknown, the probabilities would be replaced with corresponding predictive probabilities under a Bayesian predictive approach. TABLE 8 Expected costs for four decision rules of class 3 in table 7.

Kraemer (1992) discussed regression models which are used to find an optimal weighted combination of the available tests and their interactions. When the true status of the characteristic may not be directly known for the subjects, latent class analysis has been used to evaluate the accuracy of tests, for example, Alvord et al. (1988) and Qu et al. (1996). Both of the cited papers considered screening by multiple tests. Alvord et al. (1988) have assumed independence between the tests, while Qu et al.

H. 0 FIG. 1. 3), the shaded area. 3. Strategy for choosing optimal decision rules. To make decisions in a systematic manner based on the the n test outcomes, we first define what a decision rule is for the present content. If Di is the Bernoulli variate representing test i as defined in the previous section, a (nonrandomized) decision rule, which would take actions according to the values of Di's, is defined as a binary function from {O, l}n into {O, I}. Then let v = {RIR: {O, l}n ---+ {O, I}} denote the collection of all decision rules which are 22n in number.

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