Difference between revisions of "Introduction"

{{ArtBy|autore=Gianni Frisardi}}'''Abstract:''' The masticatory system, encompassing teeth, occlusion, muscles, joints, and the central nervous system, is increasingly understood as a complex system rather than a simple biomechanical mechanism. This shift in perspective aligns with Thomas Kuhn's phases of paradigm shifts, where anomalies in traditional models trigger the search for new paradigms. In the context of Masticationpedia, a new interdisciplinary approach to diagnosing and treating malocclusion emerges, focusing on "Occlusal Dysmorphisms" rather than "malocclusion."

 

 

 

Before delving into the analysis of Masticationpedia, we must first introduce some preliminary considerations, particularly regarding two fundamental dimensions—social and scientific-clinical aspect—that characterize both the present era and the one immediately preceding it.

===The phases of paradigm change according to Thomas Kuhn===

| <blockquote>'''Phase 2''', or '''Normal Science''':

During this period, the scientific community debates which paradigm to adopt. The chosen paradigm might not be the "truest" but the one gaining the most support. In Masticationpedia, a new model in masticatory rehabilitation will be introduced, focusing on data-driven inferences rather than symptom-based models.</blockquote>  

| align="right" width="250" |<small>''The black swan symbolizes one of the historical problems of epistemology: if all the swans we have seen so far are white, can we decide that all swans are white?''</small>  

| align="center" |[[File:Black_Swan_(Cygnus_atratus)_RWD.jpg|175px|center]]  

| align="center" |[[File:Duck-Rabbit illusion.jpg|203px|center]]  

| width="250" |<small>''Kuhn used optical illusion to demonstrate how a paradigm shift can cause a person to see the same information in a completely different way.''</small>  

<math>A \Rightarrow B = \lnot A \lor B</math>{{Tooltip||Let’s consider the statement: “If a person has TMDs, then they experience orofacial pain.” We can represent this in logic as <math>A \Rightarrow B = \lnot A \lor B</math>, where:<math>A</math> represents "The person has TMDs."<math>B</math> represents "The person experiences orofacial pain." In this case, "If a person has TMDs, then they experience orofacial pain" is equivalent to saying “either the person does not have TMDs (<math>\lnot A</math>), or they experience orofacial pain (<math>B</math>)”. The formula is true in the following cases: If the person does not have TMDs (<math>\lnot A</math>), the statement is true, regardless of orofacial pain. If the person has TMDs (<math>A</math>) and experiences orofacial pain (<math>B</math>), the statement is true.

'''P-value''': In medicine, for example, we rely on statistical inference to confirm experimental results, specifically the 'P-value{{Tooltip||2=The p-value represents the probability that observed results are due to chance, assuming the null hypothesis <math> H_0 </math> is true. It should not be used as a binary criterion (e.g., <math> p < 0.05 </math>) for scientific decisions, as values near the threshold require additional verification, such as cross-validation. ''p-hacking'' (repeating tests to achieve significance) increases false positives. Rigorous experimental design and transparency about all tests conducted can mitigate this risk. Type I error increases with multiple tests: for <math> N </math> independent tests at threshold <math> \alpha </math>, the Family-Wise Error Rate (FWER) is <math> FWER = 1 - (1 - \alpha)^N </math>. Bonferroni correction divides the threshold by the number of tests, <math> p < \frac{\alpha}{N} </math>, but can increase false negatives. The False Discovery Rate (FDR) by Benjamini-Hochberg is less conservative, allowing more true discoveries with an acceptable proportion of false positives. The Bayesian approach uses prior knowledge to balance prior and data with a posterior distribution, offering a valid alternative to the p-value. To combine p-values from multiple studies, meta-analysis uses methods like Fisher's: <math> \chi^2 = -2 \sum \ln(p_i) </math>. In summary, the p-value remains useful when contextualized and integrated with other measures, such as confidence intervals and Bayesian approaches.}}' a "significance test" that assesses data validity. Yet, even this entrenched concept is now being challenged. A recent study highlighted a campaign in the journal "Nature" against the use of the P-value.<ref name=":1">{{cita libro  

  }} Mar;567(7748):305-307.</ref> Signed by over 800 scientists, this campaign marks a "silent revolution" in statistical inference, encouraging a reflective and modest approach to significance.<ref name=":2">{{cita libro  

  }} Jan;65(1):1-12.</ref><ref name=":3">{{cita libro  

  }}</ref><ref name=":4">{{cita libro  

 

'''Interdisciplinarity''': Solving science-based problems increasingly demands interdisciplinary research (IDR), as underscored by the European Union’s Horizon 2020 project.<ref>European Union, ''[https://ec.europa.eu/programmes/horizon2020/en/h2020-section/societal-challenges Horizon 2020]''</ref> Yet IDR poses cognitive challenges, partly due to the dominant "Physical Paradigm of Science" that limits its recognition. The "Engineering Paradigm of Science" has been proposed as an alternative, focusing on technological tools and collaboration. Researchers need "metacognitive scaffolds"—tools to enhance interdisciplinary communication and knowledge construction.<ref name=":0">{{cita libro  

  }} Oct;129:25-39.</ref>

A superficial view might suggest a conflict between the disciplinarity of the "Physics Paradigm of Science'{{Tooltip||2=The "Physical Paradigm of Science" describes an epistemological approach prevalent in the physical sciences, focusing on deterministic models and rigorous experimental methodologies. This paradigm relies on empirical observations and the scientific method to seek universal laws governing natural phenomena.'''Key Characteristics''' 1. ''Determinism'': Assumes that natural phenomena follow fixed laws, allowing for accurate predictions based on initial conditions.

While effective in classical natural sciences, the physical paradigm has limitations in complex fields like neurophysiology, where dynamic interactions and variability challenge deterministic models. '''Application in Masticatory Neurophysiology''': In masticatory neurophysiology, the physical paradigm aids in developing basic models but falls short in explaining emergent behaviors, such as motor unit recruitment in response to complex stimuli. '''Towards an Integrated Paradigm''': Emerging is an "Engineering Paradigm of Science," which offers a more adaptive approach that considers complexity, allowing for more flexible predictive models that account for non-linear interactions in biological systems.}} (which highlights anomalies) and the interdisciplinarity of the "Engineering Paradigm of Science'{{Tooltip||The '''Engineering Paradigm of Science''' emphasizes practical applications, interdisciplinary collaboration, and the understanding of complex systems. It contrasts with traditional deterministic models, focusing instead on real-world problem-solving, particularly in fields like biology, medicine, and social sciences.'''Key Features''' ''Problem-Solving Orientation'': Prioritizes solutions to complex issues over purely theoretical models. ''Interdisciplinary Collaboration'': Encourages integration of knowledge from various disciplines, enhancing understanding through shared expertise. ''Complex Systems Focus'': Acknowledges emergent behavior and the interconnectivity of system components, recognizing that outcomes can be unpredictable and non-linear.

''Iterative Process'': Embraces an adaptive approach, refining models based on empirical data and feedback to improve responsiveness.'''Technological Integration''': Applies engineering principles to enhance research design and data analysis, utilizing simulations and computational modeling. '''Application in Masticatory Neurophysiology''' In masticatory neurophysiology, this paradigm fosters innovative diagnostic tools and treatment approaches. By integrating neurophysiology, biomechanics, and material science, it provides a comprehensive view of jaw function and dysfunction.The Engineering Paradigm of Science promotes collaboration and innovation, ultimately leading to advancements that enhance our understanding of complex systems and improve practical outcomes across various fields.}} (focused on metacognitive scaffolds). However, these perspectives are not in conflict; they are complementary and drive "Paradigmatic Innovation" in science.

File:Mechanic Silent Period.jpg|'''Figura 1d:''' Evoked mechanical silent period showing balanced neuromuscular dynamics despite malocclusion.  

Before concluding, we must clarify that the masticatory system is a "Complex System"<ref>https://en.wikipedia.org/wiki/Complex_system</ref>, not a simple biomechanical mechanism focused solely on dental occlusion. Occlusion is just one subset within a broader context that includes periodontal receptors, neuromuscular spindles, motor units, the central nervous system, and the temporomandibular joint. This interaction creates "Emergent Behavior{{Tooltip||The **masseter silent period** (MSP) is a relevant example of emergent behavior in masticatory neurophysiology. This reflex is triggered by sudden chin taps, leading to a brief cessation of electrical activity in the masseter muscle, and is closely related to the recruitment of motor units. During the MSP, there is a specific modulation of motor unit recruitment, regulated by the central nervous system, to respond to external stimuli. In the context of emergent behavior, this reflex is not limited to a single muscle but represents a coordinated response involving synergies among various neuronal centers and antagonist muscles. This integration stabilizes the mandible, adapting in real time to the force of the stimulus and producing an adaptive response. Mathematically, we can describe the probability <math>P(R)</math> of an emergent response as a function of the input variables <math>x_1, x_2, \ldots, x_n</math> that influence motor unit activation: <math>P(R) = f(x_1, x_2, \ldots, x_n)

</math> where <math>f</math> represents the non-linear interaction among incoming stimuli (such as the type and intensity of the chin tap) and the central integration processes of the trigeminal system. This model helps to understand how the MSP reflects an integrated and adaptive response that emerges from complex neurophysiological circuits rather than a single neural pathway.}}," or masticatory behavior.