A Comparison of the Scar Prevention Effect Between Carbon Dioxide Fractional Laser and Pulsed Dye Laser in Surgical Scars
Bibliographic record
Abstract
BACKGROUND: The use of ablative lasers based on the fractional approach is a novel strategy for the treatment of postoperative and acne scars in addition to wrinkles. OBJECTIVE: To evaluate and compare the efficacy of carbon dioxide ablative fractional laser (AFL) and the pulsed dye laser (PDL) for the improvement of surgical scars. MATERIALS AND METHODS: Fourteen Korean patients with surgical scars were enrolled for this study. Half of each scar was treated with a 10,600-nm AFL and the contralateral half with the 595-nm PDL. For early intervention of the postoperative scar, the laser treatments were begun after 2 weeks from the Mohs micrographic surgery. RESULTS: Both PDL and AFL produced statistically significant improvements. However, comparatively, there was no statistical difference between them. In each variable, AFL was more effective than PDL in the improvement of pliability and thickness. In contrast, PDL was superior to AFL in the improvement of vascularity and pigmentation. CONCLUSION: Pulsed dye laser and AFL treatments for surgical scar provide significant improvement. Pulsed dye laser was more effective in color of scar compared with AFL, which showed marked improvement in the contour of scar. Overall improvement was not statistically different in the Vancouver Scar Scale.
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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 | 0.001 | 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) | 0.001 | 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".