구순열 수술 후 흉터를 가진 영유아 환자에서 흉터 복합 레이저요법의 효과 및 안전성에 대한 단일 기관, 후향적 연구
Bibliographic record
Abstract
Background: Postoperative scars following cleft lip surgery are frequently accompanied by cosmetic and functional problems; therefore, patients with cleft lip scars usually undergo various long-term scar treatment courses. Currently, only a limited number of fractional laser treatment cases of cleft lip scars have been reported.Objective: This study aimed to evaluate the safety and efficacy of the combination laser treatment for hypertrophic scar management following cleft lip surgery.Methods: This single-center retrospective study included 27 patients who underwent cleft lip surgery before 12 months of age. The control group comprising 13 patients received only a silicone gel or sheet for the scars, while the treatment group comprising 14 patients underwent laser treatment with a 1,550 nm non-ablative fractional erbium-glass laser, followed by a 10,600 nm ablative fractional carbon dioxide laser. Intralesional triamcinolone injection (5∼10 mg/mL) was administered immediately after laser treatment. Scar improvement was assessed at 1, 3, and 6 months after surgery using the Vancouver Scar Scale.Results: The mean total Vancouver scar scale showed a statistically significant improvement in the treatment group compared to that in the control group. In the sub-analysis of pigmentation, the vascularity, pliability, and height significantly improved in the treatment group. No differences in the patients’ baseline characteristics between the two groups were noted. No significant adverse effects were observed during the follow-up.Conclusion: Combination laser therapy using fractional lasers and intralesional triamcinolone injection is safe and effective for managing cleft lip surgery scars in infantile patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".