Transcutaneous Electrical Nerve Stimulation (TENS) Combined with the T-Scan System
Bibliographic record
Abstract
Masticatory muscle hyperactivity has been considered a significant factor in promoting and perpetuating dysfunctional symptoms observed in Temporomandibular Disorder patients. Many therapeutic modalities have evolved within Dental Medicine that attempt to lessen or resolve the varying symptoms frequently reported by dysfunctional patients. One such method, known as Ultra Low Frequency (ULF) Transcutaneous Electrical Neural Stimulation (TENS), has been used to relax the masticatory musculature by applying an electrical stimulus to the efferent motor fibers of the Vth and VIIth cranial nerves, such that TENS can result in pain analgesia and patient sedation, restore compromised muscle physiology, and increase muscle resting length. TENS also aids in establishing a neuromuscular maxillomandibular relationship by inducing a muscularly contracted involuntary arc of closure. This chapter discusses TENS as a treatment modality for Temporomandibular Disorders, explains how to employ TENS to obtain a neuromuscular maxillomandibular relationship, and illustrates in a Clinical Case Report the use of TENS in combination with the T-Scan Computerized Occlusal Analysis System to measurably and physiologically balance a removable overlay anatomical acetyl resin orthotic prosthesis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.065 | 0.022 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".