Clinical Study Effects of the Combined PDL/Nd:YAG Laser on Surgical Scars: Vascularity and Collagen Changes Evaluated by In Vivo Confocal Microscopy
Bibliographic record
Abstract
Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The aim of this study was to investigate the efficacy of the sequential combined 585 nm PDL and the 1064 nm neodymium:yttrium-aluminium-garnet laser (PDL/Nd:YAG) in the treatment of surgical scars and to evaluate the short-term effects by in vivo confocal microscopy (RCM) and the long-term effects by clinical assessment of the scars. Twenty-five patients were enrolled with 39 postoperative linear scars; each scarwas divided into twofields.One half was treatedwith the combinedPDL/Nd:YAG laser, whereas the other half remained untreated. Each scar was treated three times at monthly intervals. Scars were evaluated by an independent examiner, using the Vancouver Scar Scale. The combined PDL/Nd:YAG laser significantly improved the appearance of the scars. In order to study the short-term effects of combined laser treatment, six additional patients were enrolled with 7 postoperative linear scars. One half of scars was treated once with the combined PDL/Nd:YAG laser. One week after this laser treatment, both the treated and the nontreated parts of the scars were examined by dermoscopy and RCM. The dermoscopic pictures revealed improvements even in treated areas. In conclusion, the combined PDL/Nd:YAG laser was found to be effective in improving the quality and appearance of the surgical scars. 1.
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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 | 0.000 |
| 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) | 0.002 | 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".