Treatment of Surgical Scars Using a 595-nm Pulsed Dye Laser Using Purpuric and Nonpurpuric Parameters: A Comparative Study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Many studies have examined laser treatment of scars, but cosmetic results have been variable. Although no studies have examined the effect of purpura on scar improvement using the pulsed dye laser (PDL), many clinicians believe inducing purpura results in better and quicker improvement. OBJECTIVE: To determine whether PDL treatment of fresh surgical scars with purpura-inducing settings improves clinical appearance more than non-purpura-inducing settings or no treatment. METHODS: Twenty-six subjects with surgical scars enrolled in this prospective study. Scars were divided into three equal segments; treatment was randomized: 595-nm PDL with purpuric (1.5 ms) or nonpurpuric (10 ms) settings or no treatment. Fluences were adjusted to Fitzpatrick skin type. Scars were treated three times, 1 month apart, beginning at suture removal. Outcome measures included Vancouver Scar Scale (VSS) and blind clinical ratings. RESULTS: The nonpurpuric condition showed significant improvement on the VSS total score, vascularity, and pliability ratings. The purpuric condition demonstrated a trend for improvement on the VSS total. According to blind observer ratings, all conditions improved, without differences between groups. CONCLUSION: Nonpurpuric settings on the PDL resulted in significant improvements in the appearance of fresh surgical scars for vascularity, pliability, and VSS total scores, although all scar segments improved over time.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 it