Optimal Timing of Fractional CO2 Laser on Cleft Lip Scars: A Single-Blind Randomized Controlled Cohort Study
Bibliographic record
Abstract
BACKGROUND Scar is one of the most common complications for patients with cleft lip. Fractional CO2 laser is an effective method to improve the appearance of scars. However, there are no published articles about the optimal time for applying. OBJECTIVE To determine the optimal time to apply a fractional CO 2 laser for the treatment of postsecondary repair scars in patients with cleft lip. METHODS Forty-two patients with linear scarring after cleft lip repair were recruited from November 2021 to October 2022. A single-blind, randomized, controlled cohort study was conducted to examine the impact of fractional CO 2 laser treatment compared with conventional conservative treatment. Thirty patients started laser treatment at 1 month ( n = 10), 3 months ( n = 10), and 6 months ( n = 10) postoperatively, and 12 patients were in the control group. Each patient was treated with high-energy low-density fractional CO 2 laser treatment 3 times at an interval of 1 month. The Vancouver Scar Scale (VSS) was used for scar evaluation to determine vascularity, pigmentation, pliability, and height. RESULTS The VSS scores decreased significantly after laser treatment ( p < .05), with the most significant improvement in scars in the group that started treatment 1 month after the surgery. CONCLUSION Early postoperative fractional CO 2 laser treatment of cleft lip scars is more effective than later treatment.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".