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Difference between revisions of "Conclusion of the ‘Normal Science’ section"
Conclusion of the ‘Normal Science’ section (view source)
Revision as of 09:21, 12 May 2024
, 6 months ago→Index Ψ {\displaystyle \Psi } at time t 0 {\displaystyle t_{0}}
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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]] | [[File:Table 1 CNSS.jpg|thumb|'''Table 1:''' representation of the output data from the RDC test in 40 subjects of which 30 asymptomatic and 10 symptomatic. For the RDC model, there are 9 subjects affected by TMDs and 1 symptomatic subject considered healthy but who in the follow up will realize that he was suffering from a serious tumor pathology, so the accuracy data will vary slightly depending on whether the false positive subject is considered or false negative, the conceptual substance does not change in this context. For the model emerging from the 'Group of Experts', however, the subjects affected by TMDs were 2, the subjects affected by other pathologies (noTMDs) were 7 and also for this model the subject n° 40 was considered not ill (healthy )]] | ||
===Index <math>\Psi</math> at time <math>t_0</math>=== | ===Index <math>\Psi</math> at time <math>t_0</math>=== | ||
We begin with an overview of the results obtained from the RDC model applied to a sample of 30 asymptomatic and 10 symptomatic subjects, analyzing the sensitivity and specificity of the test and, consequently, the total probability calculated through Bayes' theorem. This theorem is fundamental for evaluating the probability that a patient with a positive test is actually affected by the disease. | We begin with an overview of the results obtained from the RDC model applied to a sample of 30 asymptomatic and 10 symptomatic subjects, analyzing the sensitivity and specificity of the test and, consequently, the total probability calculated through Bayes' theorem. This theorem is fundamental for evaluating the probability that a patient with a positive test is actually affected by the disease. | ||
The model identified 9 symptomatic subjects affected by TMDs who met the RDC clinical criteria, and one healthy subject among the symptomatic, with a disease prevalence of | The model identified 9 symptomatic subjects affected by TMDs who met the RDC clinical criteria, and one healthy subject among the symptomatic, with a disease prevalence of 9%<ref>?</ref> in the examined population. (Table 1) We proceed to apply Bayes' theorem to the statistical data. | ||
Based on the results of the RDC test for our 40 subjects, of which 9 were considered affected by TMDs and 1 as a false negative, we proceed as follows: | Based on the results of the RDC test for our 40 subjects, of which 9 were considered affected by TMDs and 1 as a false negative, we proceed as follows: | ||