Botulinum Toxin to Improve Lower Blepharoplasty Scar: A Double-Blinded, Randomized, Vehicle-Controlled Clinical Trial
Bibliographic record
Abstract
BACKGROUND: Lower blepharoplasty is a common cosmetic operation that relies on minimal postoperative scarring, but Asian patients are at higher risk than Caucasians for hypertrophic and/or widened scars. Botulinum toxin type A (BTX) injections are widely employed to alleviate dynamic facial rhytids and also can improve scar quality by reducing scar tension. The authors assessed whether simultaneous transcutaneous lower blepharoplasty and BTX injections could improve subciliary scar quality. OBJECTIVES: The objective of this study was to assess whether simultaneous transcutaneous lower blepharoplasty and BTX injections could improve subciliary scar quality. METHODS: This is a prospective, randomized, vehicle-controlled, double-blinded clinical trial. Between May 2015 and May 2018, 40 adults who underwent bilateral transcutaneous lower blepharoplasties were randomized to receive BTX (n = 20) or vehicle (normal saline; n = 20) injections into the lateral orbicularis oculi muscle immediately after wound closure. Vancouver Scar Scale, Visual Analogue Scale, and photographic scar width measurements at 3 reference points were recorded at the final clinical follow-up. RESULTS: Thirty-seven patients completed the trial. Vancouver Scar Scale and Visual Analogue Scale scores in the experimental and vehicle control groups were similar, but scar widths in the experimental group at all measured points were significantly narrower than in the vehicle control group (P < 0.001, P = 0.027, and P < 0.001 at each measured point, respectively). CONCLUSIONS: Transcutaneous lower blepharoplasty scars in Asians can be significantly narrowed by simultaneous BTX injections without additional complications.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Randomized trial | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Randomized trial | high |
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 | 0.003 |
| 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) | 0.006 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".