Prospective, Before-After Cohort Study to Assess the Efficacy of Laser Therapy on Hypertrophic Burn Scars
Bibliographic record
Abstract
INTRODUCTION: Hypertrophic burn scars produce significant morbidity, including itching, pain, stiffness, and contracture. Best practices for management continue to evolve. Lasers have recently been added to treatment algorithms, but indications and efficacy have not been fully defined. We studied the impact of laser therapies on hypertrophic burn scars. METHODS: We conducted a prospective, before-after study in burn patients with hypertrophic scars. Procedures were performed more than 6 months after burn injury and were repeated monthly. The pulsed-dye laser was used for pruritus and erythema, whereas the fractional CO2 laser was used for stiffness and abnormal texture. All procedures were performed in the OR, with anesthesia. Outcomes are as follows: (1) Vancouver Scar Scale (objective changes in pigmentation, erythema, pliability, height; range, 0-15) and (2) UNC Scar Scale (subjective changes in pain, itching, tingling, stiffness; range, 0-12). Before-after scores were compared by Student t test, with significance assigned to P values of <0.05. RESULTS: During 2011, we treated 147 patients (mean age, 26.9 years; mean TBSA, 16.1%) over 415 sessions (2.8 sessions/patient), including pulsed dye laser (n = 327) and CO2 (n = 139), mean surface area of 83 cm. Etiology included flame (75), scald (37), and other (35). Treatments occurred 16 months (median) and 48 months (mean) after burn injury. Vancouver Scar Scale decreased from 10.4 (SD, 2.4) to 5.2 (1.9) (P < 0.0001). UNC Scar Scale decreased from 5.4 (2.5) to 2.1 (1.7) (P < 0.0001). Mean length of follow-up was 4.7 months. CONCLUSIONS: Laser therapies significantly improve both the signs and symptoms of hypertrophic burn scars, as measured by objective and subjective instruments.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".