Ablative <scp>CO</scp><sub>2</sub> fractional resurfacing in treatment of thermal burn scars: an open‐label controlled clinical and histopathological study
Bibliographic record
Abstract
INTRODUCTION: Burn scars can cause permanent disfiguring problems with limited treatments available. OBJECTIVES: To assess and correlate the clinical and histopathological effects of fractional CO2 laser on thermal burns in a controlled study. METHODS: Fifteen patients 11 with hypertrophic and four with keloidal scars received three CO2 fractional laser sessions every 4-6 weeks. Half of the scar was untreated as a control. Clinical evaluation by Vancouver, PSOAS scores, and photography before, monthly, and 3 months after the last laser session was performed. Ten patients were evaluated histopathologically by standard H&E, Masson trichrome, and Elastica von Gieson special stains. RESULTS: Hypertrophic scars (HTSs) showed textural improvement and a significant decrease of Vancouver, POSAS observer, and patient scores by the end of follow-up period in the laser-treated area (P = 0.011, 0.017 and 0.018, respectively) unlike keloidal scars. Histopathology revealed significant decrease in scar thickness in HTSs only (P < 0.001) as well as a significant decrease in collagen bundle thickness and density in the upper dermis in both types of scars. CONCLUSIONS: Fractional CO2 laser is a possible safe and effective modality for the treatment of hypertrophic burn scars with improvement achieved 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".