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Transcutaneous Electrical Nerve Stimulation (TENS) Combined with the T-Scan System

2015· book-chapter· en· W2476393901 on OpenAlexaff
Curtis Westersund

Bibliographic record

VenueAdvances in medical technologies and clinical practice book series · 2015
Typebook-chapter
Languageen
FieldHealth Professions
TopicTemporomandibular Joint Disorders
Canadian institutionsInuit Circumpolar Council
Fundersnot available
KeywordsMedicineTranscutaneous electrical nerve stimulationMasticatory forceAnesthesiaPhysical medicine and rehabilitationOrthodontics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.013
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Research integrity
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.946
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.013
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0020.006
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.037
GPT teacher head0.413
Teacher spread0.376 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2015
Admission routes1
Has abstractyes

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