Time Course Analysis of the Effects of Botulinum Neurotoxin Type A on Pain and Vasomotor Responses Evoked by Glutamate Injection into Human Temporalis Muscles
Bibliographic record
Abstract
The effect of botulinum neurotoxin type A (BoNTA) on glutamate-evoked temporalis muscle pain and vasomotor responses was investigated in healthy men and women over a 60 day time course. Subjects participated in a pre-BoNTA session where their responses to injection of glutamate (1 M, 0.2 mL) and saline (0.2 mL) into the temporalis muscles were assessed. On Day 1, BoNTA (5 U) was injected into one temporalis muscle and saline into the contralateral temporalis muscle, in a randomized order. Subjects then received intramuscular injections of glutamate (1 M, 0.2 mL) into the left and right temporalis muscles at 3 h and subsequently 7, 30 and 60 days post-injection of BoNTA. Pain intensity, pain area, and neurogenic inflammation (skin temperature and skin blood perfusion) were recorded. Prior to BoNTA treatment, glutamate evoked significantly greater pain and vasomotor reactions (P < 0.001) than saline. BoNTA significantly reduced glutamate-evoked pain intensity (P < 0.05), pain area (P < 0.01), skin blood perfusion (P < 0.05), and skin temperature (P < 0.001). The inhibitory effect of BoNTA was present at 3 h after injection, peaked after 7 days and returned to baseline by 60 days. Findings from the present study demonstrated a rapid action of BoNTA on glutamate-evoked pain and neurogenic inflammation, which is in line with animal studies.
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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) | 0.001 | 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".