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Difference between revisions of "Conclusion of the ‘Normal Science’ section"
Conclusion of the ‘Normal Science’ section (view source)
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(Created page with "<Center> <div style="text-shadow: 2px 2px 10px gray;"> =Conclusion of the ‘Normal Science’ section= </div> </Center> =Abstract= 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 wi...") |
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=Abstract= | =Abstract= | ||
[[File:Psi.jpg|left|200x200px]] | [[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. | 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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At this point, we can transfer the results of the RDC test into Bayes and quantify the probabilities of clinical positivity and/or negativity. We begin with the data exiting the first analysis of the sample based on the classic RDC model and called Index <math>\Psi</math> at time <math>t_0</math> | At this point, we can transfer the results of the RDC test into Bayes and quantify the probabilities of clinical positivity and/or negativity. We begin with the data exiting the first analysis of the sample based on the classic RDC model and called Index <math>\Psi</math> at time <math>t_0</math> | ||
[[File:Table 1 CNSS.jpg|thumb]] | |||
===Index <math>\Psi</math> at time <math>t_0</math>=== | ===Index <math>\Psi</math> at time <math>t_0</math>=== | ||
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Martina K. Shephard and Gary Heir<ref>Martina K. Shephard & Gary Heir.Orofacial Pain in the Medically Complex Patient. Contemporary Oral Medicine, 26 January 2019</ref> illustrate in their scientific article that various pathological conditions can confuse the diagnosis of Orofacial Pain, such as TMDs, including cardiovascular diseases, musculoskeletal disorders, and neurological conditions. | Martina K. Shephard and Gary Heir<ref>Martina K. Shephard & Gary Heir.Orofacial Pain in the Medically Complex Patient. Contemporary Oral Medicine, 26 January 2019</ref> illustrate in their scientific article that various pathological conditions can confuse the diagnosis of Orofacial Pain, such as TMDs, including cardiovascular diseases, musculoskeletal disorders, and neurological conditions. | ||
For this reason, we subjected the symptomatic subjects to further evaluation by a multidisciplinary team. The follow-up, concluded after two years, provided significant answers to the expressed doubts, demonstrating the complexity of medical diagnoses and the importance of a holistic approach in symptom evaluation. Indeed, the updated data were recalculated concurrently with a series of alternative statistical settings. This second step was called, appropriately, 'Index <math>\Psi</math>' at time <math>t_n</math>[[File: | For this reason, we subjected the symptomatic subjects to further evaluation by a multidisciplinary team. The follow-up, concluded after two years, provided significant answers to the expressed doubts, demonstrating the complexity of medical diagnoses and the importance of a holistic approach in symptom evaluation. Indeed, the updated data were recalculated concurrently with a series of alternative statistical settings. This second step was called, appropriately, 'Index <math>\Psi</math>' at time <math>t_n</math> | ||
[[File:Accuratezza RDC.jpg|left|thumb|'''Table 2:''' Results regarding the accuracy of the RDC test]] | |||
==Index <math>\Psi</math>' at time <math>t_n</math>== | ==Index <math>\Psi</math>' at time <math>t_n</math>== | ||
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<center> | <center> | ||
<gallery heights="240" mode="slideshow"> | <gallery heights="240" mode="slideshow"> | ||
File:Leonardo4 | File:Leonardo4 modificata.jpg|'''Figure 1:''' Interferential EMG performed in the year 2000. Trace later considered as a quantum operator '''<math>\hat{E}</math>''' | ||
File:Recovery cycle. | File:Recovery cycle.jpeg|'''Figure 2:''' Recovery Cycle of the Masseter Inhibitory Reflex (rcIMR) performed in the year 2014. Trace later considered as a quantum operator '''<math>\hat{P}</math>''' | ||
</gallery> | </gallery> | ||
</center> | </center> | ||