CO2 laser treatment for scars after cleft lip surgery: a systematic review and meta-analysis
Bibliographic record
Abstract
Current studies are controversial on the optimal treatment of postoperative scar treatment by cleft lip. Our objective is to elucidate the therapeutic effect of CO2 laser on postoperative cleft lip scar treatment. A systematic review was performed and reported according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses. We searched five electronic databases (EMBASE, PubMed, Web of Science, Cochrane Library and CNKI, from their inceptions until August 8, 2023) and independently assessed the methodological quality and bias risk of the included studies by two investigators using the Cochrane Handbook for Systematic Reviews. Quality assessment of the certainty of evidence was performed based on the Grading of Recommendations Assessment, Development, and Evaluation guidelines. Weighted mean difference of Vancouver Scar Scale were calculated to conduct meta-analysis by Stata statistical software version 14. We also estimated the pool sensitivity as well as testing the possibility of publication bias. Five studies were included in this meta-analysis involving 255 subjects. Meta-analysis showed that compared with the control group, CO2 laser was more effective in treating post-cleft lip scars (WMD = 4.39, 95%CI = 0.54–8.23; Five studies with 255 participants; Low evidentiary certainty, I2 = 99.4%). Patients treated with CO2 laser therapy for postoperative cleft lip scar treatment tend to have a significant therapeutic effect especially in the early stages. identifier CRD42023397042 (18/02/2023) [ https://www.crd.york.ac.uk/prospero/ ].
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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 | 0.016 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.040 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".