Laser therapy for treating cleft lip or/and palate scarring—a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract This review aims to assess the efficacy and safety of laser therapy in managing scars resulting from cleft lip and/or palate (CL/P) repair surgeries, as well as to determine the optimal timing for intervention. A systematic search was conducted across four databases using a predefined search strategy. Studies included were randomized controlled trials, non-randomized studies, and case series focusing on laser therapy for CL/P scars. Data extraction and analysis were performed using Revman Software. A total of two randomized controlled trials, four non-randomized studies, and three case series were included in the analysis. The fractional CO2 laser was the most commonly utilized type of laser. Following laser therapy, there was a significant decrease in Vancouver Scar Scale (VSS) scores by 4.05 (95% CI, 2.10–5.99). Meta-analysis revealed that laser treatment groups exhibited a significantly lower mean VSS score (1.3; 95% CI, 0.02–2.67) compared to control groups. Moreover, initiating laser therapy intervention at one month postoperatively resulted in a significantly lower VSS score compared to initiation at three months postoperatively (difference of 1.70; 95% CI, 1.33–2.08). No severe complications were reported. Laser therapy demonstrates effectiveness and safety in improving CL/P scars, with earlier intervention yielding greater benefits.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.019 |
| Bibliometrics | 0.008 | 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".