Effect of botulinum toxin type A on muscular temporomandibular disorder: A systematic review and meta‐analysis of randomized controlled trials
Bibliographic record
Abstract
BACKGROUND: Botulinum toxin type A (BTX-A) is increasingly used to manage painful temporomandibular disorders (TMD). However, the effect of BTX-A on muscular TMD remains unclear. OBJECTIVE: To assess the efficacy, safety and optimal dose of BTX-A for treating TMD. METHODS: We conducted systematic literature searches in MEDLINE, Embase, Web of Science, ClinicalTrials.gov and Cochrane Library until March 2023. We extracted data from randomized controlled trials (RCTs) that evaluated the efficacy and safety of BTX-A in treating muscular TMD. We performed a meta-analysis using a random-effects model. RESULTS: Fifteen RCTs involving 504 participants met the inclusion criteria. BTX-A was significantly more effective than placebo in reducing pain intensity, as measured on a 0-10 scale, at 1 month (MD [95% CI] = -1.92 [-2.87, -0.98], p < .0001) and 6 months (MD [95% CI] -2.08, [-3.19 to -0.98]; p = .0002). A higher dosage of BTX-A (60-100 U bilaterally) was associated with a greater reduction in pain at 6 months (MD [95% CI] = -2.98 [-3.52, -2.44]; p < .001). BTX-A also resulted in decreased masseter muscle intensity (μV) (MD [95% CI] = -44.43 [-71.33, -17.53]; p = .001) at 1 month and occlusal force (kg) at 3 months (MD [95% CI] = -30.29 [-48.22 to -12.37]; p = .0009). There was no significant difference in adverse events between BTX-A and placebo. CONCLUSIONS: BTX-A is a safe and effective treatment for reducing pain and improving temporomandibular muscle and joint function in muscular TMD patients. A bilateral dose of 60-100 U might be an optimal choice for treating muscular TMD pain.
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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 | 0.017 | 0.040 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.025 | 0.040 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".