Efficacy and safety of botulinum toxin type A for postoperative scar prevention and wound healing improvement: A systematic review and meta‐analysis
Bibliographic record
Abstract
BACKGROUND: Botulinum toxin type A (BTXA) has shown underlying effects for wound healing improvement. New small clinical trials keep emerging every year and updated evidence-based information is warranted. This study aimed to evaluate the efficacy and safety of BTXA for preventing scarring. METHODS: Four databases were searched to recruit randomized clinical trials (RCTs) which compared the surgical wounds treated with BTXA vs. those treated with placebo or blank control. The outcomes were primarily quantified by measures including the Vancouver Scar Scale (VSS), Visual Analog Scale (VAS), Stony Brook Scar Evaluation Scales (SBSES), modified SBSES (mSBSES), and scar width. Patients' satisfaction and adverse events were also reported. RESULTS: In total, 16 RCTs involving 671 cases (510 patients) were included. The outcome showed significant superiority of BTXA in VSS (mean difference [MD] = -1.32, 95% confidence interval [CI]: -2.00 to -0.65, p = 0.0001), VAS (MD = 1.29, 95% CI: 1.05-1.52, p < 0.00001), SBSES or mSBSES (MD = -0.18, 95% CI: -0.27 to -0.10, p < 0.0001), scar width (MD = -0.18, 95% CI: -0.27 to -0.10, p < 0.0001), and patients' satisfaction (risk ratio [RR] = 1.25, 95% CI: 1.06-1.49, p = 0.01). No significant difference of adverse events incidence was observed (RR = 1.46 95% CI: 0.64-3.33, p = 0.36). CONCLUSIONS: Botulinum toxin type A is effective and safe for postoperative scar prevention and wound healing improvement, especially for facial wounds of Asians. Further studies should manage to standardize the treatment algorithm, while mSBSES is recommended for scar assessment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.009 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".