Laser Therapy to Treat Hypertrophic Scars in Children with Burn Injuries
Bibliographic record
Abstract
Introduction: Laser therapy has emerged as a valuable treatment option for hypertrophic burn scars. Currently, the two most commonly used lasers include the ablative fractional CO2 laser to improve scar thickness and stiffness and the pulsed dye laser to reduce scar redness. However, research regarding the use of laser therapy in the pediatric population has been limited by poorly designed studies. Thus, the aim of this study was to investigate the effectiveness of using laser therapy to improve hypertrophic burn scars in pediatric patients using a comprehensive set of subjective and objective scar assessment tools. Methods: A single-centre, prospective observational study was carried out at a tertiary pediatric hospital. Twenty participants with hypertrophic burn scars that had not received previous laser treatment were included. Laser therapy sessions were administered over the course of one year and all treatment parameters were tailored to meet the needs of each participant. A comprehensive set of scar assessment tools including the Vancouver Scar Scale, the Patient and Observer Scar Assessment Scale, conventional ultrasound, acoustic radiation force impulse ultrasound elastography, and the DermaLab Combo® colour and elasticity probes were used to evaluate scar characteristics at each study visit. Results: Seventy-one laser procedures were carried out with the majority of participants receiving treatment with both the ablative fractional CO2 laser and the pulsed dye laser at the same session (83%). All participants received at least three sessions of laser therapy with no complications noted. From baseline to study completion, statistically significant improvements in all scar measures, both subjective and objective, were observed (p
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".