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Effect of experimental jaw‐muscle pain on the spatial distribution of surface EMG activity of the human masseter muscle during tooth clenching

2011· article· en· W1801834118 on OpenAlexaboutno aff
Tommaso Castroflorio, Deborah Falla, Kelun Wang, Peter Svensson, Dario Farina

Bibliographic record

VenueJournal of Oral Rehabilitation · 2011
Typearticle
Languageen
FieldHealth Professions
TopicTemporomandibular Joint Disorders
Canadian institutionsnot available
Fundersnot available
KeywordsMasseter muscleBite force quotientMedicineTemporal muscleElectromyographyTemporalis muscleOrthodonticsDentistryAnatomyPhysical medicine and rehabilitation

Abstract

fetched live from OpenAlex

This study tested the hypothesis that painful injections of glutamate into the human masseter muscle differentially affect the distribution of the electromyographic (EMG) activity in the masseter muscle at rest and during tooth clenching. Surface EMG signals were recorded bilaterally from the superficial masseter of nine healthy men with a grid of 32 electrodes, before and after intramuscular injection of glutamate or isotonic saline, during rest and isometric contractions at 20%, 40%, 60% and 80% of the maximal voluntary bite force. Intramuscular injection of glutamate evoked moderate pain (0-10 visual analogue scale: 6·4 ± 1·4), with sensory-discriminative characteristics of the perceived pain, evaluated with the use of the McGill Pain Questionnaire (MPQ), similar to those previously reported for patients with temporomandibular disorders. There was no effect of the glutamate injection on EMG amplitude during rest, whereas during tooth clenching, the spatial distribution of the masseter EMG activity on both sides was more uniform in the painful condition compared to the control condition. Moreover, the overall EMG amplitude decreased on both sides during the more forceful tooth clenching following glutamate injection. In conclusion, a unilateral painful stimulation was associated with a bilateral inhibition of the masseter muscles during tooth clenching which resulted in a more uniform distribution of EMG activity.

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)0.005
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.694
Threshold uncertainty score0.286

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.031
GPT teacher head0.359
Teacher spread0.327 · 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; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations36
Published2011
Admission routes1
Has abstractyes

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