Local Injection of Botulinum Toxin Type A to Prevent Postoperative Scar
Bibliographic record
Abstract
BACKGROUND: Physical scars, especially those in the head and neck area, can cause emotional and psychological distress. Recent studies, have suggested that botulinum toxin type A (BTX-A), also known as Botox, may improve surgical scars by speeding up the wound healing process. Injection of BTX-A is generally considered a less invasive approach. OBJECTIVES: The purpose of this meta-analysis was to assess the efficacy and safety of BTX-A in the prevention of postoperative scars compared to placebo or no treatment. MATERIALS AND METHODS: Following databases were searched from inception to March 2019: Cochrane Library, EMBASE, Web of Science, PubMed, and Open grey. Five trials registers were searched for potentially related trials. The authors also searched reference lists of relevant articles and contacted the investigators to identify additional published and unpublished studies. DATA COLLECTION AND ANALYSIS: Two authors independently evaluated all potential studies that met the selection criteria. Two authors independently extracted and analyzed the data. RESULTS: Analysis was conducted on 267 patients who were enrolled in trials and randomly assigned to receive local injection of BTX-A (184 patients) and placebo (182 patients). Improved Vancouver scar scale scores were noted among patients treated with BTX-A injections compared with the control group (P = .000). The visual analogue scale scores revealed a significant improvement in appearance for the BTX-A-treated scars (P = .000). In addition, lower increase in width of the wound was observed in the experimental group compared to the control group (P = .000). RECOMMENDATION: This systematic review provided preliminary evidence that supports the efficacy and safety of BTX-A for the prevention of postoperative scar.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".