Botulinum toxin type A for preventing and treating cleft lip scarring—A systematic review and meta‐analysis
Bibliographic record
Abstract
BACKGROUND: Cleft lip and/or palate (CL/P) are congenital cleft facial deformities that are abnormal developments caused by errors in the fusion process of the embryo's face. Surgery is an important treatment, but postoperative scars will still cause psychological shadows to patients. This study aimed to systematically evaluate the efficacy of Botulinum toxin type A (BTXA) in preventing and treating postoperative CL/P scars and improving scar quality. METHODS: A systematic review was performed by searching PubMed, EMBASE, the Cochrane Library, and Web of Science for relevant trials. All relevant trials were performed before June 30, 2021. The data were entered into Revman 5.3 software, and a meta-analysis was conducted by using the random-effects model or fixed-effects model. RESULTS: Four randomized controlled trials involving 161 cases were included. Through quantitative analysis, BTXA showed significant differences in preventing and treating postoperative CL/P scars in terms of scar width (MD: -0.20; [95% CI, -0.30, -0.10], p < 0.0001) and the Visual Analog Scale (VAS) (MD: 1.30; [95% CI, 1.06, 1.55], p < 0.0001), although no significant difference was noted on the Vancouver Scar Scale (VSS) (MD: -0.75; [95% CI, -1.68, 0.19], p = 0.12) between the two groups. CONCLUSION: In preventing and treating postoperative CL/P scar hypertrophy, we found that BTXA injection can show better results. There was no statistically significant difference between the results after omitting Navarro's study or Chang's study because of the time of injection-before/during surgery or adult CL/P scars.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.027 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".