Laser Therapy for Prevention and Treatment of Pathologic Excessive Scars
Bibliographic record
Abstract
BACKGROUND: The management of hypertrophic scars and keloids remains a therapeutic challenge. Treatment regimens are currently based on clinical experience rather than substantiated evidence. Laser therapy is an emerging minimally invasive treatment that has recently gained attention. METHODS: A meta-analysis was conducted to evaluate the effectiveness of various laser therapies. The pooled response rate, pooled standardized mean difference of Vancouver Scar Scale scores, scar height, erythema, and pliability were reported. RESULTS: Twenty-eight well-designed clinical trials with 919 patients were included in the meta-analysis. The overall response rate for laser therapy was 71 percent for scar prevention, 68 percent for hypertrophic scar treatment, and 72 percent for keloid treatment. The 585/595-nm pulsed-dye laser and 532-nm laser subgroups yielded the best responses among all laser systems. The pooled estimates of hypertrophic scar studies also showed that laser therapy reduced total Vancouver Scar Scale scores, scar height, and scar erythema of hypertrophic scars. Regression analyses of pulsed-dye laser therapy suggested that the optimal treatment interval is 5 to 6 weeks. In addition, the therapeutic effect of pulsed-dye laser therapy is better on patients with lower Fitzpatrick skin type scores. CONCLUSIONS: This study presents the first meta-analysis to confirm the efficacy and safety of laser therapy in hypertrophic scar management. The level of evidence for laser therapy as a keloid treatment is low. Further research is required to determine the mechanism of action for different laser systems and to examine the efficacy in quantifiable parameters, such as scar erythema, scar texture, degrees of symptom relief, recurrence rates, and adverse effects.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.011 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".