Resumo

VINIEGRA-VELAZQUEZ, Leonardo. The causality-chance binomial: explanatory principle of scientific knowledge in medicine?. Bol. Med. Hosp. Infant. Mex. [online]. 2023, vol.80, n.2, pp.94-104.  Epub 12-Jun-2023. ISSN 1665-1146.  https://doi.org/10.24875/bmhim.22000119.

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.

Palavras-chave : Statistical normality; Closed systems; Binomial causality-chance; Open systems; Mechanistic causality; Contextual causality.

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