Non-Surgical Interventions for Reducing Cleft Lip Scars: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Objective Cleft lip scars can significantly impact patients’ facial esthetics and psychological well-being. This meta-analysis aimed to evaluate the effectiveness of various non-surgical interventions in reducing cleft lip scars. Methods A systematic search was performed across PubMed, Embase, Scopus, Cochrane, and Web of Science databases up to May 21, 2024. Inclusion criteria encompassed human studies with cohort or randomized controlled trial (RCT) designs that assessed non-surgical interventions for reducing cleft lip scarring following surgery. Articles in any language, regardless of publication date, were considered. Eligible studies underwent quality assessment, and data were extracted for an inverse variance random-effects meta-analysis. Results Of 1664 initially identified articles, 14 met the inclusion criteria for review, with 8 included in the meta-analysis. The reviewed studies primarily focused on botulinum toxin type A (BTA) and laser treatments as non-surgical approaches. Meta-analysis revealed significant improvement in scar appearance with laser therapy ( P < .001) but not with BTA ( P = .15) when the assessment was conducted by the Vancouver scar scale (VSS). BTA also had no significant effect on scar width reduction ( P > .05), but improved scar appearance based on the subjective visual analogue scale (VAS) assessment ( P < .001). Conclusions Based on the VSS index, laser therapy improved scar appearance more effectively than BTA. However, BTA enhanced esthetics as measured subjectively by VAS. These findings support non-surgical interventions as a viable approach for managing cleft lip 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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.039 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| 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".