Effect of experimental jaw‐muscle pain on the spatial distribution of surface EMG activity of the human masseter muscle during tooth clenching
Bibliographic record
Abstract
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.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".