Intramuscular pH modulates glutamate‐evoked masseter muscle pain magnitude in humans
Bibliographic record
Abstract
BACKGROUND: This study was conducted to determine whether glutamate-evoked jaw muscle pain is modulated by the acidity and temperature of the solution injected. METHODS: Thirty two participants participated and received injections of high-temperature acidic (HT-A) glutamate (pH 4.8, 48 °C), high-temperature neutral (HT-N) glutamate (pH 7.0, 48 °C) and neutral temperature neutral (NT-N) glutamate (pH 7.0, 38 °C) solutions (0.5 mL) into the masseter muscle. Pain intensity was assessed with an electronic visual analogue scale (eVAS). Numerical rating scale (NRS) scores of unpleasantness and temperature perception, pain-drawing areas, mechanical sensitivity and pressure pain thresholds (PPT) were also measured. Participants filled out the McGill Pain Questionnaire (MPQ). One or two way ANOVAs were used for data analyses. RESULTS: Injection of HT-A glutamate solutions significantly increased the area under the VAS-time curve compared with injection of HT-N glutamate and NT-N glutamate solution (p < 0.040). The duration of glutamate-evoked pain was significantly longer when HT-A glutamate was injected than when NT-N glutamate was injected (p < 0.017). No significant effects of acidity were detected on pain drawings, NRS unpleasantness and heat perception, but there was a significant effect of acidity on MPQ scores and mechanical sensitivity. CONCLUSION: Acidity and temperature modulate glutamate-evoked jaw muscle pain suggesting an interaction between acid sensing and glutamate receptors which could be of importance for understanding clinical muscle pain conditions.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".