Effectiveness of Botulinum Toxin-A on Face, Head, and Neck Scars: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Botulinum toxin A (BTA) temporarily paralyzes nearby muscles to reduce tension in wound sites, inhibiting scar hyperplasia. Objective: To evaluate the effectiveness of BTA injection on scar formation and quality in various face, head, and neck sites. Methods: A comprehensive search was conducted across four electronic databases and registries to identify relevant studies. We assessed the following outcomes: visual analog scale (VAS), Vancouver scar scale (VSS), scar width, patient self-assessment scale, Stony Brook scar evaluation scales, Observer scar assessment scale, Manchester scar scale, and patient scar-assessment scale. Results: This systematic review included 20 studies encompassing 894 patients, of which, 18 studies were eligible for meta-analysis. The VAS and VSS significantly improved with BTA compared to controls which significantly reduced scar width at the first and second measurement points compared to controls. Subgroup analyses revealed that BTA had better upper lip and forehead outcomes. Conclusion: This systematic review and meta-analysis found that scars of the face, head, and neck were improved with BTA treatment compared to controls. This highlights the need for further study, especially concentrating on the upper lip and forehead regions, where improved outcomes were identified on subgroup analysis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.021 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".