A Comparative Analysis of Botulinum Toxin Use Versus Other Therapies for Temporomandibular Disorders: A Systematic Review
Bibliographic record
Abstract
Existing literature regarding the efficacy of Botulinum toxin A (BoNT-A) therapy in improving the clinical outcomes of temporomandibular disorders (TMDs) is ambiguous and lacks consistency. Thus, this study aimed to evaluate the efficacy of BoNT-A in reducing pain, occlusal force, electromyographic (EMG) changes, and maximum mouth opening compared with placebo and other interventions. An electronic database search was conducted using MEDLINE, PubMed, Google Scholar, the Cochrane Library, and ClinicalTrials.gov from January 2000 to June 2024 to identify randomized controlled trials (RCTs). A manual search complemented the electronic search. The Risk of Bias 2 (RoB 2) assessment was used to evaluate the internal validity of the included studies. A total of 1719 studies were identified, of which 23 fulfilled the inclusion criteria. Nineteen of these studies evaluated pain levels (primary outcome) after BoTN-A therapy, with six of them observing a decrease. In terms of secondary outcomes, seven of 10 studies noted an increase in maximum mouth opening, while all six reported a drop in EMG activity, and all four found a decrease in occlusal force following BoNT-A therapy. Muscle activity and biting force were significantly reduced in the therapeutic groups. Clinicians must consider these adverse events before treating patients with BoNT-A therapy. Additional regulatory guidelines and standardization of injection protocols are essential in improving therapeutic outcomes and patient safety. These findings suggest that BoNT-A may be a feasible option for TMD management but should be used with caution in clinical settings.
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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 | 0.012 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.013 |
| Bibliometrics | 0.015 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".