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===Second Clinical Approach===

===<translate>Second Clinical Approach</translate>===

''(hover over the images)''

''(<translate>hover over the images</translate>)''

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<gallery mode="packed-hover" widths="250" heights="182" perrow="3">

<gallery mode="packed-hover" widths="250" heights="182" perrow="3">

File:Spasmo emimasticatorio.jpg|'''Figure 1:''' Patient reporting "Orofacial pain in the right hemilateral)

File:Spasmo emimasticatorio.jpg|'''<translate>Figure</translate> 1:''' <translate>Patient reporting "Orofacial pain in the right hemilateral"</translate>

File:Spasmo emimasticatorio ATM.jpg|'''Figure 2:''' Patient's TMJ Stratigraphy showing signs of condylar flattening and osteophyte

File:Spasmo emimasticatorio ATM.jpg|'''<translate>Figure</translate> 2:''' <translate>Patient's TMJ Stratigraphy showing signs of condylar flattening and osteophyte</translate>

File:Atm1 sclerodermia.jpg|'''Figure 3:''' Computed Tomography of the TMJ

File:Atm1 sclerodermia.jpg|'''<translate>Figure</translate> 3:''' <translate>Computed Tomography of the TMJ</translate>

File:Spasmo emimasticatorio assiografia.jpg|'''Figure 4:''' Axiography of the patient showing a flattening of the chewing pattern on the right condyle

File:Spasmo emimasticatorio assiografia.jpg|'''<translate>Figure</translate> 4:''' <translate>Axiography of the patient showing a flattening of the chewing pattern on the right condyle</translate>

File:EMG2.jpg|'''Figure 5:''' EMG Interferential Pattern. Overlapping upper traces corresponding to the right masseter, lower to the left masseter.

File:EMG2.jpg|'''<translate>Figure</translate> 5:''' <translate>EMG Interferential Pattern. Overlapping upper traces corresponding to the right masseter, lower to the left masseter</translate>.

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<br />So be it then <math>P(D)</math> the probability of finding, in the sample of our <math>n</math> people, individuals who present the elements belonging to the aforementioned set <math>D=\{\delta_1,\delta_2,...,\delta_n\}</math>

<br /><translate>So be it then <math>P(D)</math> the probability of finding, in the sample of our <math>n</math> people, individuals who present the elements belonging to the aforementioned set <math>D=\{\delta_1,\delta_2,...,\delta_n\}</math></translate>

In order to take advantage of the information provided by this dataset, the concept of partition of causal relevance is introduced:

<translate>In order to take advantage of the information provided by this dataset, the concept of partition of causal relevance is introduced</translate>:

====The partition of causal relevance====

====<translate>The partition of causal relevance</translate>====

:Always be <math>n</math> the number of people we have to conduct the analyses upon, if we divide (based on certain conditions as explained below) this group into <math>k</math> subsets <math>C_i</math> with <math>i=1,2,\dots,k</math>, a cluster is created that is called a "partition set" <math>\pi</math>:

:<translate>Always be <math>n</math> the number of people we have to conduct the analyses upon, if we divide (based on certain conditions as explained below) this group into <math>k</math> subsets <math>C_i</math> with <math>i=1,2,\dots,k</math>, a cluster is created that is called a "partition set" <math>\pi</math></translate>:

:<math>\pi = \{C_1, C_2,\dots,C_k \}  \qquad \qquad \text{with} \qquad \qquad C_i \subset n , </math>

:<math>\pi = \{C_1, C_2,\dots,C_k \}  \qquad \qquad \text{with} \qquad \qquad C_i \subset 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>.

<translate>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></translate>.

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

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

#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, clinical signs and elements belonging to the set <math>D=\{\delta_1,\delta_2,...,\delta_n\}</math>. A causally relevant partition of this type is said to be '''homogeneous'''.

#<translate>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, clinical signs and elements belonging to the set <math>D=\{\delta_1,\delta_2,...,\delta_n\}</math>. A causally relevant partition of this type is said to be '''homogeneous'''</translate>.

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

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

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 20 to 70. 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 and precisa in '[[The logic of classical language]]'.

<translate>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 20 to 70. 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 and precisa in '[[The logic of classical language]]'</translate>.

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