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Difference between revisions of "Conclusion of the ‘Normal Science’ section"

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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=}}
'''Abstract:'''This chapter marks the conclusion of the 'Normal Science' phase, addressing key diagnostic issues in the complex field of Orofacial Pain (OP) and Temporomandibular Disorders (TMDs). Through a critical examination of current diagnostic paradigms such as the RDC/TMD, the chapter highlights the challenges of distinguishing TMD from other systemic diseases. A 5-year clinical study forms the basis for a new diagnostic model, the 'Index Ψ,' which integrates classical Bayesian probability with quantum models to address diagnostic uncertainty.


=Abstract=
Bayes' Theorem was applied to the data from the RDC/TMD model, showing a high probability (81%) of diagnosing TMD in symptomatic patients. However, the study's follow-up revealed the presence of serious non-TMD pathologies in many patients, leading to interference that reduced the diagnostic accuracy to 9.56%. This discrepancy was mathematically represented by a quantum interference term, introducing the idea of non-commutative diagnostic variables. The order of diagnostic tests was shown to influence outcomes, suggesting the need for a quantum-inspired approach to complex medical diagnoses.
[[File:Figure Psi for CNSS.jpg|left|200x200px]]
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.


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.
The study concludes by emphasizing the limits of deterministic diagnostic models and advocating for a flexible, multidisciplinary approach that accounts for the complex interplay between different clinical conditions. Future research should explore the integration of quantum models in medical diagnostics to improve accuracy and patient outcomes in multifactorial conditions like TMDs.
 
{{ArtBy|||autore=Gianni Frisardi|autore2=Giorgio Cruccu|autore3=Luca Fontana|autore4=Cesare Iani|autore5=|autore6=Diego Centonze|autore7=Manuel Luci|autore8=Flavio Frisardi|autore9=}}


==Introduction==
==Introduction==
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Bayes' theorem is an important principle of probability theory that allows us to update beliefs or probabilities about an event in light of new evidence or information. In other words, it allows us to recalculate the probability of a hypothesis, given the observation of some data.
Bayes' theorem is an important principle of probability theory that allows us to update beliefs or probabilities about an event in light of new evidence or information. In other words, it allows us to recalculate the probability of a hypothesis, given the observation of some data.


The formula for Bayes' Theorem is:
The formula for Bayes' Theorem is:  


<math>P(A|B) = \frac{P(B|A) \cdot P(A)}{P(B)}</math>
<math>P(A|B) = \frac{P(B|A) \cdot P(A)}{P(B)}</math>
Gianni
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