Difference between revisions of "Store:LPLes04"

Store:LPLes04 (view source)

Revision as of 10:13, 11 March 2023

775 bytes added , 1 year ago

no edit summary

Editor, Editors, USER, admin, Bureaucrats, Check users, dev, editor, founder, Interface administrators, oversight, Suppressors, Administrators, translator

10,784

edits

@@ Line 1: / Line 1: @@
-==Probabilistic-causal analysis==
+==Análisis probabilístico-causal==
-From these premises it is clear that the clinical diagnosis is made using the so-called hypothetical-deductive method referred to as DN<ref name=":1">{{Cite book
+De estas premisas se desprende que el diagnóstico clínico se realiza mediante el denominado método hipotético-deductivo denominado DN<ref name=":1">{{Cite book
   | autore = Sarkar S
   | titolo = Nagel on Reduction
@@ Line 16: / Line 16: @@
   | LCCN =
   | OCLC =
-  }}</ref> ([[wikipedia:Deductive-nomological_model|deductive-nomological model]]<ref>''<!--52-->DN model of scientific explanation'', <!--53-->also known as ''<!--54-->Hempel's model'', ''Hempel–Oppenheim model'', ''Popper–Hempel model'', <!--55-->or ''<!--56-->covering law model''</ref>). But this is not realistic, since the medical knowledge used in clinical decision-making hardly contains causal deterministic laws to allow causal explanations and, hence, to formulate clinical diagnoses, among other things in the specialist context. Let us try to analyse again the case of our Mary Poppins, this time trying a probabilistic-causal approach.
+  }}</ref> ([[wikipedia:Deductive-nomological_model|modelo deductivo-nomológico]])<ref>''<!--52-->DN model of scientific explanation'', <!--53-->also known as ''<!--54-->Hempel's model'', ''Hempel–Oppenheim model'', ''Popper–Hempel model'', <!--55-->or ''<!--56-->covering law model''</ref>. Pero esto no es realista, ya que el conocimiento médico utilizado en la toma de decisiones clínicas difícilmente contiene leyes deterministas causales que permitan explicaciones causales y, por tanto, formular diagnósticos clínicos, entre otras cosas en el contexto del especialista. Intentemos analizar nuevamente el caso de nuestra Mary Poppins, esta vez intentando un enfoque probabilístico-causal.
-Let us consider a number <math>n</math> of individuals including people who report Orofacial Pain who generally have bone degeneration of the Temporomandibular Joint. However, there may also be other apparently unrelated causes. We must mathematically translate the 'relevance' that these causal uncertainties have in determining a diagnosis.
+Consideremos un número <math>n</math> de personas, incluidas las personas que informan dolor orofacial que generalmente tienen degeneración ósea de la articulación temporomandibular. Sin embargo, también pueden existir otras causas aparentemente no relacionadas. Debemos traducir matemáticamente la 'relevancia' que estas incertidumbres causales tienen para determinar un diagnóstico.
+=== The casual relevance ===
+To do this we consider the degree of causal relevance <math>(cr)</math> of an event <math>E_1</math> with respect to an event <math>E_2</math> where:
+*<math>E_1</math> = patients with bone degeneration of the temporomandibular joint.
+*<math>E_2</math> = patients reporting orofacial pain.
+*<math>E_3</math> = patients without bone degeneration of the temporomandibular joint.
+We will use the conditional probability <math>P(A \mid B)</math>, that is the probability that the <math>A</math> event occurs only after the event <math>B</math> has already occurred.
+With these premises the causal relevance <math>cr</math> of the sample <math>n</math> of patients is:
-===The casual relevance===
 To do this we consider the degree of causal relevance <math>(cr)</math> of an event <math>E_1</math> with respect to an event <math>E_2</math> where:
 *<math>E_1</math> = patients with bone degeneration of the temporomandibular joint.