Botulinum Toxin to Improve Results in Cleft Lip Repair: A Double-Blinded, Randomized, Vehicle-Controlled Clinical Trial
Bibliographic record
Abstract
BACKGROUND: Most patients with facial scarring would value even a slight improvement in scar quality. Botulinum toxin A is widely used to alleviate facial dynamic rhytides but is also believed to improve scar quality by reducing wound tension during healing. The main objective was to assess the effect of Botulinum toxin on scars resultant from standardized upper lip wounds. METHODS: In this double-blinded, randomized, vehicle-controlled, prospective clinical trial, 60 consecutive consenting adults undergoing cleft lip scar revision (CLSR) surgery between July 2010 and March 2012 were randomized to receive botulinum toxin A (n = 30) or vehicle (normal saline; n = 30) injections into the subjacent orbicularis oris muscle immediately after wound closure. Scars were independently assessed at 6-months follow-up in blinded fashion using: Vancouver Scar Scale (VSS), Visual Analogue Scale (VAS) and photographic plus ultrasound measurements of scar widths. RESULTS: 58 patients completed the trial. All scar assessment modalities revealed statistically significantly better scars in the experimental than the vehicle-control group. CONCLUSION: Quality of surgical upper lip scars, which are oriented perpendicular to the direction of pull of the underlying orbicularis oris muscle, is significantly improved by its temporary paralysis during wound healing. TRIAL REGISTRATION: ClinicalTrials.gov NCT01429402.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 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) | 0.008 | 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".