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}}</ref> ([https://en.wikipedia.org/wiki/Deductive-nomological_model deductive-nomological model]<ref>''DN model of scientific explanation'', also known as ''Hempel's model'', the ''Hempel–Oppenheim model'', the ''Popper–Hempel model'', or the ''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> ([https://en.wikipedia.org/wiki/Deductive-nomological_model deductive-nomological model]<ref>''DN model of scientific explanation'', also known as ''Hempel's model'', the ''Hempel–Oppenheim model'', the ''Popper–Hempel model'', or the ''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.
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.
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.
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:
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_1</math> = patients with bone degeneration of the temporomandibular joint.
*<math>E_2</math> = patients reporting orofacial pain.
*<math>E_2</math> = patients reporting orofacial pain.