Difference between revisions of "Introduction"

{{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 {{Tooltip|P-value|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  

A superficial view might suggest a conflict between the disciplinarity of the "{{Tooltip|Physics Paradigm of Science|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 "{{Tooltip|Engineering Paradigm of Science|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.

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 " {{Tooltip|Emergent Behavior|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)