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<blockquote>[[File:Question 2.jpg|50x50px|link=https://wiki.masticationpedia.org/index.php/File:Question_2.jpg|left]]'''<math>K_{brain}</math>: The uncertainty of the measurement ''' | <blockquote>[[File:Question 2.jpg|50x50px|link=https://wiki.masticationpedia.org/index.php/File:Question_2.jpg|left]]'''<math>K_{brain}</math>: The uncertainty of the measurement ''' | ||
All true and, among other things, the arguments are very engaging from an intellectual point of view, but we should take into account the series of assertions such as neuronal convergence, the inhibition of downregulation mechanisms of descending pain,<ref name=":1" /><ref name=":2" /> allodynia,<ref name=":3" /> and the concept of measure that inevitably incorporates an uncertainty. We reported a very interesting study ( | All true and, among other things, the arguments are very engaging from an intellectual point of view, but we should take into account the series of assertions such as neuronal convergence, the inhibition of downregulation mechanisms of descending pain,<ref name=":1" /><ref name=":2" /> allodynia,<ref name=":3" /> and the concept of measure that inevitably incorporates an uncertainty. We reported a very interesting study ([[Exploring electroencephalography with a model inspired by quantum mechanics]]) which demonstrated the existence of an error in the measurement of the EEG by defining a similar Heisenberg uncertainty principle called <math>K_{brain}</math> quasi-quantum model which led to a constant minimum value of uncertainty in the EEG measurement at <math>\Delta x(t)\Delta p_x(t)</math> and <math>\Delta y(t)\Delta p_y(t)</math> of <math>0,78\pm0,41\tfrac{cm^2}{4ms}</math>. Note that the unit of <math>\tfrac{cm^2}{4ms}</math> is the result of sampling the EEG at 250 Hz and taking mass as the unit. This should make us reflect in interpreting the results of laboratory research because, as we will see in the presentation of subsequent clinical cases, the diagnostic error is around the corner. Enough <math>0,78\pm0,41\tfrac{cm^2}{4ms}</math> error in the specific measurement of the neuronal district under examination to make a diagnosis of Orofacial Pain when instead there is a brain tumor that involved the same nervous district and simulates the symptoms of Orofacial Pain from Temporomandibular Disorders. | ||
</blockquote>Therefore, objectivity, scientific humility and a change of mindset in the interpretation of biological phenomena are needed, a topic that we will address in the 'Extraordinary Science' section At this stage, however, it is advisable to sort out the contents by resuming the already anticipated references regarding the classification of Orofacial Pain and DTM but in a more specific way to address the clinical cases to follow. Temporomandibular disorders (TMD) are a group of musculoskeletal and neuromuscular conditions affecting the masticatory muscles, temporomandibular joint (TMJ) and other associated structures.<ref name=":4" /> According to the diagnostic criteria for TMD (DC/TMD), as already reported, in 'Axis I', TMD could be divided into intra-articular disorders, including disc displacement, arthralgia, arthritis and osteoarthritis, and muscle disorders.<ref name=":4" /> The latter are also referred to as “myogenic TMDs”, which can be further classified into: local myalgia, if the pain is localized on palpation; myofascial pain, if the pain spreads within the palpated muscle territory; and myofascial pain with referral, if the pain spreads beyond the border of the masticatory muscles.<ref name=":4" /> | |||
<blockquote>[[File:Question 2.jpg|50x50px|link=https://wiki.masticationpedia.org/index.php/File:Question_2.jpg|left]][[File:Hephaptic edited.jpeg|thumb|200x200px|'''Figure 1:''' Trasmissione efaptica|link=https://wiki.masticationpedia.org/index.php/File:Hephaptic_edited.jpeg]]'''Machine language logic''' | |||
With regard to "myogenic TMDs" it is not as simple as the description of the CDR appears because, as we have highlighted for our poor patient '[[Encrypted code: Ephaptic transmission|Mary Poppins]]', the muscle pain and bone deconstruction of the ATM had concealed, in a logic of classical language, a much more serious organic damage that beyond the classifications it was possible to resolve, after 10 years of pilgrimage among various specialists. Only by acquiring a machine language logic was it possible to interpret the encrypted code of the 'Ephaptic Transmission'. (Figure 1) Having said this, classifications are welcome but not the use of a verbal language logic which remains, however, a vague phenomenon and ambiguous. Formal language logics such as mathematics are certain in the sense that equation <math>x^2=-1</math> has no solutions in the set of real numbers, because in this set there are no numbers whose square is negative. The value <math>i</math> is then defined, called the imaginary unit, which has the following property: <math>i^2=-1.</math> The equation <math>x^2=\pm1</math> does not exist in mathematics as it does in medical diagnostics. Without going into overly specialized topics which, however, we will address in the 'Extraordinary Science' section, in a logic of verbal language the uncertainty is much higher than that which occurs in a logic of machine language because Poppins could be affected (as they are things went) from myalgia, TMD, vasculitis, Morphea or from hemimasticatory spasm while the 'Ephaptic transmission' remains forever an organic damage and the clinical interpretation cannot be dichotomous as <math>1^2</math> cannot be <math>\pm1</math> but only <math>-1</math>. | |||
</blockquote> | |||
A recent systematic review and meta-analysis, with a combined sample of 2518 subjects, suggested that the prevalence of TMD could range from 25.2% to 34.9%,<ref>Bueno C.H., Pereira D.D., Pattussi M.P., Grossi P.K., Grossi M.L. Gender differences in temporomandibular disorders in adult populational studies: A systematic review and meta-analysis. J. Oral Rehabil. 2018;45:720–729. doi: 10.1111/joor.12661</ref> with a predominance of the myofascial pain diagnosis (10.3- 15.4%) [2]. While a study by Javed Ashraf et al.<ref name=":6">Javed Ashraf,Matti Närhi, Anna Liisa Suominenand Tuomas Saxlin. Association of temporomandibular disorder-related pain with severe headaches—a Bayesian view. Clin Oral Investig. 2022; 26(1): 729–738. Published online 2021 Jul 5. doi: 10.1007/s00784-021-04051-y. PMCID: PMC8791898. PMID: 34224000 | |||
</ref> using Bayesian methodology, aimed to examine the association of TMD-related pain with severe headaches (migraine and TTH) over an 11-year follow-up period compared with the frequency approach. Frequentist statistics suffer from some limitations, most notably the reliance on large sample sizes to accurately determine effect sizes.<ref name=":5">Buchinsky FJ, Chadha NK. To P or not to P: backing Bayesian statistics. Otolaryngol Head Neck Surg. 2017;157(6):915–918. doi: 10.1177/0194599817739260</ref> Furthermore, contrary to the Frequentist methodology, Bayesian statistics do not provide a (fixed) result value but rather an interval containing the regression coefficient.<ref>Depaoli S, van de Schoot R. Bayesian analyses: where to start and what to report. Eur Heal Psychol. 2014;16:75–84.</ref> These intervals, called credible intervals (CI), place a probability on the best estimate among all possible values of the parameter estimates.<ref name=":5" /> | |||
<blockquote>[[File:Question 2.jpg|50x50px|link=https://wiki.masticationpedia.org/index.php/File:Question_2.jpg|left]]'''Quantum Probability''' | |||
We agree with the considerations that emerged in the study by Buchinsky et al.<ref name=":6" /> because perhaps or fortunately we will never be able to create a formal language logic such as mathematics given the intrinsic randomness of biological models. Even the Bayes models, however, incorporate a conceptual limit which, if exceeded, would improve the probabilistic data and contextually the predictive value <math>P(M|Pos) | |||
</math> in the output. Briefly, Bayes' formula looks like this: | |||
</math> in | |||
<math>P(M|Pos)=\frac{P(Pos|M)\cdot P(M)}{P(Pos|M)\cdot P(M)+P(Pos|S)\cdot P(S)} | <math>P(M|Pos)=\frac{P(Pos|M)\cdot P(M)}{P(Pos|M)\cdot P(M)+P(Pos|S)\cdot P(S)} | ||
</math> | </math> | ||
It can therefore be noted that in order to calculate the predictive value of the test, it is also necessary to know the probability with which the disease affects the overall population '''<math>P(M)</math>'''. Therefore, a good test is a test with sensitivity and specificity very close to 0 and we all know that this is impossible and even wrong in some ways, however, it would be a paradigmatic test. The scarce added value, in terms of information, that tumor markers, for example, provide for diagnosis, represents the rationale for which their use as a screening test in an unselected population is not recommended. The same could happen for predictive values regarding TMD culminating in a massive classification of patients and an inevitable search for the truth by the RDC project. | |||
Without going into specialized topics, we try to briefly describe the rationale for this statement by pointing out, mainly, the differences between a classical and a quantum probabilistic model. (for more but very specialized information, see '[[Quantum-like modeling in biology with open quantum systems and instruments]]') | |||
Therefore, in the closed probability (CP) the probability distribution <math>B</math> can be computed from probability <math>A</math> and conditional probabilities <math>P(B=\beta|A=\alpha)</math>. In quantum probability (QP), the classical Total Probability Formula (FTP) is perturbed by the interference term (Khrennikov, 2010);<ref>Khrennikov A. Ubiquitous Quantum Structure: From Psychology To Finances Springer, Berlin-Heidelberg-New York(2010)</ref> for the dichotomous quantum observables <math>A</math> and <math>B</math> of von Neumann type, i.e. given by the Hermitian operators <math>\hat{A}</math> and <math>\hat{B}</math>, the quantum version of FTP has the form: | |||
{{:F:Krennikov1a}}<math>+2\sum_{\alpha_1<\alpha_2}\cos\theta_{\alpha_1\alpha_2}\sqrt{P(A=\alpha_1)P(B=\beta|A=\alpha_1)} P(A=\alpha_2) | {{:F:Krennikov1a}}<math>+2\sum_{\alpha_1<\alpha_2}\cos\theta_{\alpha_1\alpha_2}\sqrt{P(A=\alpha_1)P(B=\beta|A=\alpha_1)} P(A=\alpha_2) | ||
P(B=\beta|a=\alpha_2)</math> | P(B=\beta|a=\alpha_2)</math> | ||
If the interference term is positive, then the QP computation would generate a higher probability than its CP counterpart given by the classical FTP. In particular, this probability amplification underlies the supremacy of quantum computing. There are numerous statistical data from cognitive psychology, decision making, molecular biology, genetics and epigenetics demonstrating that biosystems, from proteins and cells (Asano et al., 2015b)<ref>Asano M., Khrennikov A., Ohya M., Tanaka Y., Yamato I. Quantum Adaptivity in Biology: From Genetics To Cognition Springer, Heidelberg-Berlin-New York(2015)</ref> to humans (Khrennikov, 2010,<ref>Khrennikov A. Ubiquitous Quantum Structure: From Psychology To Finances Springer, Berlin-Heidelberg-New York(2010)</ref> Busemeyer and Bruza, 2012<ref>Busemeyer J., Bruza P. Quantum Models of Cognition and Decision Cambridge Univ. Press, Cambridge(2012)</ref>) use this amplification and operate with non-CP updates. | |||
</blockquote>{{q2|With this somewhat courageous and risky preface we have highlighted the complexity of diagnostic processes and models especially in the presence of multifactorial pathologies such as Orofacial Pain and DTM that we will present in the next chapters}}{{Bib}} |
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