Changes

where with the symbolism <math>C_i \subset n </math> it indicates that the subclass <math>C_i</math> is contained in <math>n</math>.

where with the symbolism <math>C_i \subset n </math> it indicates that the subclass <math>C_i</math> is contained in <math>n</math>.

The partition <math>\pi</math> in order for it to be defined as a partition of causal relevance it must have these properties:

The partition <math>\pi</math> in order for it to be defined as a partition of causal relevance it must have these properties:

#For each subclass <math>C_i</math> the condition must apply <math>rc=P(D \mid C_i)- P(D )\neq 0, </math>ie the probability of finding in the subgroup <math>C_i</math> a person who has the symptoms and elements belonging to the set <math>D=\{\delta_1,\delta_2,...,\delta_n\}</math>it cannot be identical to that which one would have in finding it 'fishin'" in the whole sample <math>n</math>. A causally relevant partition of this type is said to be '''homogeneous'''.

#For each subclass <math>C_i</math> the condition must apply <math>rc=P(D \mid C_i)- P(D )\neq 0, </math> ie the probability of finding in the subgroup <math>C_i</math> a person who has the symptoms and elements belonging to the set <math>D=\{\delta_1,\delta_2,...,\delta_n\}</math> it cannot be identical to that which one would have in finding it 'fishin'" in the whole sample <math>n</math>. A causally relevant partition of this type is said to be '''homogeneous'''.

#Each subset <math>C_i</math> must be 'elementary', ie it must not be further divided into other subsets, as if these existed they would have no causal relevance.

#Each subset <math>C_i</math> must be 'elementary', ie it must not be further divided into other subsets, as if these existed they would have no causal relevance.

Now let us assume, for example, that the population sample <math>n</math> to which our good patient Mary Poppins belongs is a category of subjects aged between 20 and 70 years. We also assume that in this population we have those who present the elements belonging to the data set <math>D=\{\delta_1,.....\delta_n\}</math>which correspond to the laboratory tests mentioned above.

Now let us assume, for example, that the population sample <math>n</math> to which our good patient Mary Poppins belongs is a category of subjects aged between 20 and 70 years. We also assume that in this population we have those who present the elements belonging to the data set <math>D=\{\delta_1,.....\delta_n\}</math> which correspond to the laboratory tests mentioned above.

Let us suppose that in a sample of 10,000 subjects from 20 to 70 we will have an incidence of 30 subjects <math>p(D)=0.003</math> showing clinical signs <math>\delta_1</math> and <math>\delta_4

Let us suppose that in a sample of 10,000 subjects from 20 to 70 we will have an incidence of 30 subjects <math>p(D)=0.003</math> showing clinical signs <math>\delta_1</math> and <math>\delta_4

*

*

 

{{q2|A homogeneous partition provides what we are used to calling Differential Diagnosis.|}}

<br />{{q2|A homogeneous partition provides what we are used to calling Differential Diagnosis.|}}

These conditional probabilities demonstrate that each of the partition's four subclasses is causally relevant to patient data <math>D=\{\delta_1,.....\delta_n\}</math> in the population sample <math>PO</math>. Given the aforementioned partition of the reference class, we have the following clinical situations.

These conditional probabilities demonstrate that each of the partition's four subclasses is causally relevant to patient data <math>D=\{\delta_1,.....\delta_n\}</math> in the population sample <math>PO</math>. Given the aforementioned partition of the reference class, we have the following clinical situations.

Mary Poppins <math>\in</math> no degeneration of the temporomandibular joint <math>\cap</math> Temporomandibular Disorders

Mary Poppins <math>\in</math> no degeneration of the temporomandibular joint <math>\cap</math> Temporomandibular Disorders

Mary Poppins <math>\in</math> no degeneration of the temporomandibular joint <math>\cap</math> no Temporomandibular Disorders.

Mary Poppins <math>\in</math> no degeneration of the temporomandibular joint <math>\cap</math> no Temporomandibular Disorders.

To arrive at the final diagnosis above, we conducted a probabilistic-causal analysis of Mary Poppins' health status whose initial data were <math>D=\{\delta_1,.....\delta_n\}</math>. In general we can indicate a following logical process:

To arrive at the final diagnosis above, we conducted a probabilistic-causal analysis of Mary Poppins' health status whose initial data were <math>D=\{\delta_1,.....\delta_n\}</math>. In general we can indicate a following logical process:

*a base probability <math>P(D)=0,003</math>

*a base probability <math>P(D)=0,003</math>

At this point we should introduce too specialized arguments that would take the reader off the topic but that have an high epistemic importance for which we will try to extract the most described logical thread of the Analysandum / Analysans concept.

At this point we should introduce too specialized arguments that would take the reader off the topic but that have an high epistemic importance for which we will try to extract the most described logical thread of the Analysandum/Analysans concept.

The probabilistic-causal analysis of <math>D=\{\delta_1,.....\delta_n\}</math> it is, therefore, a couple of the following logical forms Analysandum / Analysans<ref>Westmeyer, H. (1975). [https://psycnet.apa.org/record/1976-01749-001 The diagnostic process as a statistical-causal analysis. ''Theory and Decision'']'', 6''(1), 57–86. <nowiki>https://doi.org/10.1007/BF00139821</nowiki></ref> which is:

The probabilistic-causal analysis of <math>D=\{\delta_1,.....\delta_n\}</math> it is, therefore, a couple of the following logical forms Analysandum / Analysans<ref>Westmeyer, H. (1975). [https://psycnet.apa.org/record/1976-01749-001 The diagnostic process as a statistical-causal analysis. ''Theory and Decision'']'', 6''(1), 57–86. https://doi.org/10.1007/BF00139821</ref> which is:

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