High- Versus Low-Density Fractional Laser in the Treatment of Hypertrophic Postburn Scars: A Randomized Clinical Trial
Bibliographic record
Abstract
BACKGROUND: Fractional CO2 laser has been shown effective in improving pigmentation, pruritus, and tightness of hypertrophic burn scars. However, there is no consensus on the optimal treatment parameters. OBJECTIVE: To compare effectiveness of different densities of fractional CO2 laser in the treatment of mature hypertrophic burn scars. MATERIALS AND METHODS: The study included 25 patients, each with 3 or more mature hypertrophic burn scars. Scars were randomly assigned to treatment with low-, medium-, and high-density fractional CO2 laser. Each scar received 3 sessions of laser at 1-month interval. The degree of improvement was assessed clinically using Vancouver Scar Scale (VSS) and Patient and Observer Scar Assessment Scale (POSAS) scores, and histologically through evaluation of collagen (Masson's Trichrome stain) before and 1 month after end of therapy. RESULTS: High-density parameters showed significant higher improvement in VSS and POSAS assessment scores (p-value < .001). Pliability and relief are the most improved parameters. Histopathological evaluation revealed a significant drop in the mean area percent of collagen in the 3 used parameters, with highest improvement with high-density laser treatment (p-value < .001). CONCLUSION: High-density fractional CO2 laser treatment provides more improvement in burn scars both clinically and histopathologically.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".