Factors Influencing Reduced Scar Tissue Formation Following Unilateral Cleft Lip Plastic Surgeries: a Systematic Literature Review
Bibliographic record
Abstract
Objectives: Reduction of postoperative scarring after unilateral cleft lip cheiloplasty is a pertinent topic. Smaller scar formation results not only in a better lip function, but also - aesthetics. This systematic review aims to identify various supplementary treatment options which influence favourable scarring outcome after cheiloplasty. Material and Methods: The systematic review was conducted following PRISMA guidelines and Cochrane methodologies, using databases including PubMed, ScienceDirect, The Cochrane Library, and Google Scholar. Articles published between 2012 and 2024 evaluating supplementary measures for reducing scar tissue post-unilateral cleft lip cheiloplasty were included. Studies were assessed for bias using the Joanna Briggs Institute Critical Appraisal Checklist, and outcomes were measured using Vancouver Scar Scale, Visual Analog Scale, Hollander Wound Evaluation Score, and scar width metrics. Results: Nine studies were analysed, highlighting platelet-rich plasma and botulinum toxin A as effective in reducing scar width and improving aesthetics. Silicone-based products enhanced wound healing, while non-absorbable sutures and tissue adhesives showed superior outcomes compared to absorbable sutures. Overall, supplementary measures significantly improved postoperative scar appearance. Conclusions: The use of platelet-rich plasma or botulinum toxin injections in musculus oribucalaris oris during the cheiloplasty significantly improves postoperative scar formation outcome. Wound edges approximation by non-absorbable sutures or tissue glue equally results in smaller scar formation and better aesthetic outcome. Suturing with absorbable sutures increases the incidence of complications and results in poor aesthetics. Postoperative wound care with products containing silicone significantly reduces scarring and aids in aesthetics.
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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 | 0.006 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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".