<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>1665-1146</journal-id>
<journal-title><![CDATA[Boletín médico del Hospital Infantil de México]]></journal-title>
<abbrev-journal-title><![CDATA[Bol. Med. Hosp. Infant. Mex.]]></abbrev-journal-title>
<issn>1665-1146</issn>
<publisher>
<publisher-name><![CDATA[Instituto Nacional de Salud, Hospital Infantil de México Federico Gómez]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1665-11462023000200094</article-id>
<article-id pub-id-type="doi">10.24875/bmhim.22000119</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[El binomio causalidad-azar: ¿principio explicativo del conocimiento científico en medicina?]]></article-title>
<article-title xml:lang="en"><![CDATA[The causality-chance binomial: explanatory principle of scientific knowledge in medicine?]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Viniegra-Velázquez]]></surname>
<given-names><![CDATA[Leonardo]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Hospital Infantil de México Federico Gómez Unidad de Investigación en Medicina Basada en Evidencias Departamento de Investigación Educativa]]></institution>
<addr-line><![CDATA[ Ciudad de México]]></addr-line>
<country>México</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>04</month>
<year>2023</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>04</month>
<year>2023</year>
</pub-date>
<volume>80</volume>
<numero>2</numero>
<fpage>94</fpage>
<lpage>104</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S1665-11462023000200094&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S1665-11462023000200094&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S1665-11462023000200094&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen Este ensayo cuestiona las verdades matemáticas como principio explicativo del conocimiento científico médico. Se analiza, en primer término, el concepto de normalidad actual basado en una distribución de valores probabilísticos, y se destacan sus limitaciones y equívocos para captar la complejidad de la condición humana. Los sistemas cerrados (juegos de azar), origen de la teoría de las probabilidades y del binomio causalidad-azar, se comparan con los sistemas abiertos propios del proceso vital y se argumentan sus diferencias extremas. Se destaca el despropósito de depositar en el binomio causalidad-azar el significado de asociaciones entre sucesos propios de la complejidad de la vida humana en salud y enfermedad. Se confrontan las características de la causalidad mecanicista (puntual, lineal, unidireccional, homogénea y fija), que equipara al organismo con una máquina y es la única explicación científica aceptada del acontecer de la vida humana, con las de la causalidad contextual (difusa, heterogénea, jerárquica, multidireccional y cambiante), que especifica diversos órdenes causales interactuantes que dan forma a la condición humana: el histórico, el social, el político, el económico, el cultural o el biológico, que representa una mirada escrutadora y penetrante de la complejidad de los seres humanos. Se concluye la superioridad de la causalidad contextual sobre la mecanicista, que abre posibilidades explicativas de sucesos vitales que suelen arrumbarse como «efectos del azar». Esta aproximación integradora a la complejidad humana puede enriquecer y fortalecer el método clínico, hoy degradado y en riesgo de extinción.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract This essay questions mathematical truths as an explanatory principle of the medical scientific knowledge. It analyzes, in the first place, the current concept of normality based on a distribution of probabilistic values and its limitations and mistakes to capture the complexity of the human condition are highlighted. The closed systems (gambling) origin of the theory of probabilities and the binomial causality-chance are compared with open systems typical of the complexity of the vital process, and their extreme differences are argued. The nonsense of depositing in the causality-chance binomial the meaning of associations between events typical of the complexity of human life in health and disease is highlighted. The characteristics of mechanistic causality (punctual, homogeneous, linear, unidirectional and fixed), which equates the organism with a machine and is the only accepted scientific explanation of events of human life, are confronted with those of contextual causality (diffuse, heterogeneous, hierarchical, multidirectional and changing), which specifies various interacting causal orders that shape of human condition: the historical, the social, the political, the economical, the cultural or the biological that represents a scrutinizing and penetrating look at the complexity of human beings. It concludes the superiority of contextual causality over mechanistic causality that opens up explanatory possibilities of the vital events that are usually put away as "effects of chance". This integrative approach to the human complexity can enrich and strengthen the clinical method that is now degraded and at risk of extinction.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Normalidad estadística]]></kwd>
<kwd lng="es"><![CDATA[Sistemas cerrados]]></kwd>
<kwd lng="es"><![CDATA[Binomio causalidad-azar]]></kwd>
<kwd lng="es"><![CDATA[Sistemas abiertos]]></kwd>
<kwd lng="es"><![CDATA[Causalidad mecanicista]]></kwd>
<kwd lng="es"><![CDATA[Causalidad contextual]]></kwd>
<kwd lng="en"><![CDATA[Statistical normality]]></kwd>
<kwd lng="en"><![CDATA[Closed systems]]></kwd>
<kwd lng="en"><![CDATA[Binomial causality-chance]]></kwd>
<kwd lng="en"><![CDATA[Open systems]]></kwd>
<kwd lng="en"><![CDATA[Mechanistic causality]]></kwd>
<kwd lng="en"><![CDATA[Contextual causality]]></kwd>
</kwd-group>
</article-meta>
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