Early Postoperative Treatment of Surgical Scars Using a Fractional Carbon Dioxide Laser: A Split-Scar, Evaluator-Blinded Study
Bibliographic record
Abstract
BACKGROUND: Although focus has recently been directed toward the early treatment of surgical scars, the optimal time at which to initiate treatment with fractional laser and its effect on scar remodeling remains controversial. OBJECTIVES: To assess the safety and efficacy of treating surgical scars using an ablative carbon dioxide (CO2 ) fractional laser during the early postoperative period. MATERIALS AND METHODS: We performed a prospective, split-scar, evaluator-blinded study on 16 postoperative scars of 15 patients. Patients began treatment 3 weeks after surgery and were treated in two sessions of CO2 fractional laser therapy on half of the scar at 2-week intervals. All patients were followed for 3 months after the final treatment session. RESULTS: Three months after the last treatment, a greater decrease in Vancouver Scar Scale score was noted in the treated half of the scars, especially in terms of texture and thickness. Patients also expressed a significantly greater degree of satisfaction with the treated side as assessed using a subjective 4-point scale. Only one patient experienced any adverse effect, which was the development of hypertrophy, on the treated and untreated side of the scar. CONCLUSION: CO2 fractional laser is an effective treatment modality for surgical scars in the early postoperative period.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".