Fractionally Ablative Er:YAG Laser Resurfacing for Thermal Burn Scars: a Split-Scar, Controlled, Prospective Cohort Study
Bibliographic record
Abstract
BACKGROUND: Thermal burn scars can have catastrophic impact on the quality of life and personal image, and over time can lead to profound physical and psychological debilitation. There are no established treatments to significantly improve burn scars. OBJECTIVE: To demonstrate the safety, efficacy, and tolerability of fractionally ablative Er:YAG resurfacing of mature burn scars. METHODS: Sixteen subjects were enrolled and received 3 treatments of fractionally ablative Er:YAG resurfacing at monthly intervals. Twelve completed the study. Scars were scored with the Vancouver Scar Scale (VSS) by the patient and physician before and after treatment. Blinded photographic analysis (Visual Analog Scale [VAS]) and blinded histologic analysis of tissue before and after treatment was also performed. RESULTS: Significant Improvement in VSS scores were seen in all 12 patients, reported by patients and the evaluating physician alike. Photographic analysis demonstrated subjective improvement in all 12 patients. Histologically, there was significant improvement in collagen architecture and the number of vessels per high-power field. The treatments were tolerated well by patients, and 1 superficial skin infection occurred. CONCLUSION: Fractionally ablative Er:YAG laser resurfacing is a safe and effective modality in the treatment of thermal burn scars with subjective and objective improvement as seen from the patient and physician.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".