Efficacy of botulinum toxin type A injections in improving hypertrophic scarring and keloid formation: A systematic review and meta-analysis of randomized controlled trials
Bibliographic record
Abstract
Introduction: This article aims to provide a systematic review of the use of botulinum toxin type A (BTA) in the prevention and treatment of hypertrophic scars and keloids. These types of scars pose significant challenges in clinical practice, and alternative treatment approaches are being explored. BTA has shown promise in its potential to modulate scar formation and improve outcomes. Material and Methods: Following the guidelines set forth by the preferred reporting items for systematic reviews and meta-analyses, a thorough examination of the available literature was conducted, encompassing the period from the inception of relevant databases until September 2023. The electronic databases utilized for this review included CENTRAL, MEDLINE, Google Scholar, and EMBASE. Results: Our review evaluated 1001 articles, ultimately including 12 randomized controlled trials that fulfilled our inclusion criteria. The visual analog scale (VAS) scores revealed a significant improvement in the cosmetic outcomes for the BTA group (mean difference [MD] 1.03, 95% confidence interval [CI] 0.01–2.05, P < 0.0001). Similarly, the vancouver scar scale (VSS) scores indicated superior scar quality in the BTA group (MD = −1.18, 95% CI −1.94 to −0.42, P = 0.001). Adverse events were minimal and included instances such as mild eyelid drooping and the development of an abscess requiring surgical intervention. Conclusion: Our systematic review and meta-analysis indicate that BTA significantly improves hypertrophic scars and keloids, as shown by better VAS and VSS scores. Adverse events were minimal. Further large-scale studies are needed for validation.
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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 | 0.019 | 0.046 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.030 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".