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| =Abstract=
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| [[File:Figure Psi for CNSS.jpg|left|200x200px]]
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| This chapter details the use of Bayes' Theorem in diagnosing Temporomandibular Disorders (TMD) using the RDC (Research Diagnostic Criteria) classification criteria. The analysis focuses on determining the sensitivity and specificity of the diagnostic test, calculating the overall probability that a patient with a positive test result is actually affected by TMD based on a disorder prevalence of 9% in the examined population. The Bayes model is used to update diagnostic probabilities based on new clinical evidence. Key elements of the model include: 'Prevalence' <math>P(A)</math>: The frequency with which the TMD condition occurs in the general population, estimated at 9%; 'Sensitivity' <math>P(B|A)</math>: The probability that the diagnostic test correctly identifies a patient affected by TMD as such; 'Specificity' <math>P(\neg B|\neg A)</math>: The probability that the test correctly excludes those not affected by TMD. The Bayes' Theorem formula is as follows: <math>P(A|B) = \frac{P(B|A) \cdot P(A)}{P(B)}</math> This formula is used to calculate the post-test probability that a patient is affected by TMD given a positive test result. We use data collected from 40 subjects undergoing the RDC test: 9 subjects were identified as affected by TMD and 1 subject was a false negative. The calculation method is based on total probability and conditional probability to determine the test's effectiveness in correctly diagnosing TMD. Concerns are raised about the possibility that other serious pathologies could mimic TMD symptoms, potentially confusing test results. Therefore, the need for a thorough and multidisciplinary follow-up to verify the reliability of test results and to exclude other medical conditions that might present similar symptoms is emphasized.
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| The discussion concludes by emphasizing the importance of adopting a diagnostic approach that integrates the best practices and methodologies available. It is suggested that the adoption of quantum models in addition to the traditional Bayes model could significantly improve the accuracy of medical diagnoses, providing clinicians with more robust tools to interpret diagnostic test results and manage diseases more effectively.
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| {{ArtBy|||autore=Gianni Frisardi|autore2=Giorgio Cruccu|autore3=Luca Fontana|autore4=Cesare Iani|autore5=|autore6=Diego Centonze|autore7=Manuel Luci|autore8=Flavio Frisardi|autore9=}} | | {{ArtBy|||autore=Gianni Frisardi|autore2=Giorgio Cruccu|autore3=Luca Fontana|autore4=Cesare Iani|autore5=|autore6=Diego Centonze|autore7=Manuel Luci|autore8=Flavio Frisardi|autore9=}} |