Treatment of postburn hypertrophic scaring in skin of color with fractional CO2 laser - A prospective cohort study
Bibliographic record
Abstract
Objective To evaluate the efficacy of fractional CO2 laser therapy in treating mature hypertrophic burn scars. Method A prospective cohort study enrolled burn patients with postburn hypertrophic scars undergoing fractional CO2 laser treatment in Rawalpindi, Pakistan. Patients aged 12 to 80 years were included, receiving 4 laser sessions every 4-6 weeks. Demographic data and scar assessments using the Vancouver Scar Scale and Patient Observer Scar Assessment Scale were collected. Results Twenty-five patients with hypertrophic scars received treatment. Vancouver Scar Scale scores showed significant reductions, with improvements in scar vascularity (pre: 0.85 ± 1.085, post: 0.10 ± 0.300, P < .001), pigmentation (pre: 2.44 ± 0.673, post: 2.12 ± 0.900, P = .008), and pliability (pre: 2.29 ± 1.078, post: 1.39 ± 0.997, P < .001). Patients with Fitzpatrick skin types III and IV had notable Vancouver Scar Scale score improvements ( P = .013, P < .001). Patient Observer Scar Assessment Scale scores also decreased significantly post-treatment ( P < .001). Conclusion Fractional CO2 laser therapy shows promise in managing mature hypertrophic burn scars, with improvement in scar appearance, functionality, and symptom relief. Stratification by Fitzpatrick skin type highlights the need for further research to optimize treatment strategies, particularly in populations with darker skin tones. This study underscores the importance of further longitudinal studies on burn scars to enhance outcomes for all burn survivors.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".