Effects of Botulinum Toxin on Improving Facial Surgical Scars: A Prospective, Split-Scar, Double-Blind, Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Early intervention might improve the quality of surgical scars. Botulinum toxin type A has been shown to improve surgical scars in the past decade. The purpose of this study was to evaluate the effect of botulinum toxin type A on surgical facial scars. METHODS: In this prospective, split-scar, double-blind, randomized controlled trial, 16 consecutive patients who underwent facial surgery between June and October of 2015 were enrolled. Botulinum toxin type A was injected randomly into half of each surgical wound closure immediately after surgery. The scars were assessed independently by two plastic surgeons at a 6-month follow-up visit using the Vancouver Scar Scale and the visual analogue scale. The scar width was also measured. RESULTS: Fourteen patients completed the study. The visual analogue scale score and scar width measurements revealed a significant improvement in appearance and narrower scars for the botulinum toxin type A-treated halves of the scars (p = 0.046 and p = 0.001, respectively). The mean Vancouver Scar Scale score was 4.68 for the botulinum toxin type A-injected group and 5.24 for the control group (p = 0.15). In addition, the Vancouver Scar Scale height score was significantly different between the two groups (p = 0.008). CONCLUSION: This study demonstrates that early postsurgical botulinum toxin injections can produce better, narrower, and flatter facial surgical scars. CLINICAL QUESTION/LEVEL OF EVIDENCE: Therapeutic, II.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".