585-nm Pulsed Dye Laser in the Treatment of Surgical Scars Starting on the Suture Removal Day
Bibliographic record
Abstract
BACKGROUND: The optimal time frame to improve the quality and cosmetic appearance of scars by laser therapy has not been clearly elucidated by prior controlled clinical trials. OBJECTIVE: To determine the efficacy of the 585-nm pulsed dye laser (PDL) in the treatment of surgical scars starting on the day of suture removal. METHODS: Eleven patients (skin types I-IV) with 12 postoperative linear scars that were greater than 2 cm were treated three times on monthly intervals with the 585-nm PDL (450 micro s, 10-mm spot size, 3.5 J/cm2 with 10% overlap) on one scar half, whereas the other half received no treatment. Scars were later evaluated by a blinded examiner using the Vancouver Scar Scale (VSS) for pigmentation, vascularity, pliability, and height. Scars were then blindly examined for cosmetic appearance using a visual analog scale. RESULTS: One month after the last treatment, final scar analysis by the blinded examiner revealed a significant difference between treated and untreated sites, with the treated halves scoring better in all scar parameters in the VSS and in cosmetic appearance. The treated halves demonstrated an overall average improvement in the VSS between the first treatment score and the final score of 54% versus 10% in the controls (P=0.0002). The cosmetic appearance score (0=worst; 10=best) at final assessment was significantly better for the treated scars, scoring 7.3 versus the averaged control score of 5.2 (P=0.016). CONCLUSION: The 585-nm PDL is effective and safe in improving the quality and cosmetic appearance of surgical scars in skin types I-IV starting on the day of suture removal.
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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 | 0.000 | 0.000 |
| 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".