The Safety and efficacy of botulinum toxin type A injection for postoperative scar prevention: A systematic review and meta‐analysis
Bibliographic record
Abstract
BACKGROUND: Active prevention and treatment of scars are particularly important. Several studies have used botulinum toxin type A(BTXA) to prevent postoperative scarring. The aim of this systematic review and meta-analysis was to systematically evaluate the efficacy and safety of BTXA in preventing and treating postoperative scars. METHODS: A computer-based search was conducted for the five databases including PubMed, Cochrane Library, EMBASE, CNKI, and Wanfang up to May 22, 2019, to collect the relevant literatures on BTXA treatment of postoperative hypertrophic scars. A meta-analysis was made with the software of Revman 5.3 based on the study endpoint of scar width, Vancouver Scar Scale (VSS), Visual Analogue Scale (VAS) scores, and patient satisfaction as well. RESULTS: A total of 18 randomized controlled trials (RCTs) studies were included with 915 patients in all. The result showed that, compared with the control group, the scar width, VAS scores, and VSS scores of the BTXA group were significantly improved and higher patient satisfaction was achieved. CONCLUSION: BTXA has a certain curative effect on postoperative scar prevention and treatment without obvious side effects.
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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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.027 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 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".