Mitigation of Postsurgical Scars Using Lasers: A Review
Bibliographic record
Abstract
BACKGROUND: Most postsurgical scars are considered esthetically and functionally acceptable. Currently, there is no definite consensus treatment for postsurgical scarring. The purpose of this review is to shed some light on the value of scar mitigation and the efficacy of different lasers employed on postsurgical wounds. METHODS: A systematic literature review and computational analysis were conducted to identify relevant clinical articles that pertained to the use of lasers for mitigating postsurgical scars. Articles included the National Institutes of Health-National Center for Biotechnology Information-PubMed search and sources cited from relevant studies after 1995. Trials that attributed pre- and posttreatment scores of scar severity based on a verified scar evaluation scale (eg, Patient and Observer Scar Assessment Scale, Vancouver Scar Scale, Global Assessment Scale) were chosen. Clinical assessments varied for each study. To adequately assess the efficacy of the modalities, the final scaled scar appearance scores were realigned and normalized to a standard scale for unbiased comparison. RESULTS: After filtering through a total of 124 studies, 14 relevant studies were isolated and thus included in the review. Studied lasers were as follows: Pulsed dye laser (PDL), carbon dioxide, diode, potassium titanyl phosphate (KTP), and erbium glass (Er-Glass) lasers. CONCLUSION: Treatment with lasers in the postsurgical wound healing phase is safe, effective, and advised in mitigation of pathologic scar formation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".