A systematic review of procedural treatments for burn scars in children: Evaluating efficacy, safety, standard protocols, average sessions and tolerability based on clinical studies
Bibliographic record
Abstract
Abstract Managing burn scars in children presents significant challenges. This study investigates effective treatment methods for burn scars, focusing on efficacy, safety, standard protocols and tolerability. Major databases such as PubMed, Scopus and Web of Science were thoroughly searched up to August 2024, emphasizing procedural treatments for burn scars in children. Key data collected included participant demographics, sample sizes, intervention methods, follow‐up protocols, treatment effectiveness and reported adverse events. A total of 256 children were assessed, with all procedural treatments yielding satisfactory outcomes. Among the various methods, trapeze‐flap plasty and percutaneous collagen induction showed improvements in all patients. In the laser treatment group, which included 161 children, the Vancouver Scar Scale (VSS) score reduction ranged from 55.55% to 76.31%, with outcomes rated as good (24.61%) to excellent (60%). Laser treatment using local anaesthesia proved to be well tolerated by children. Our findings indicate that various methods—including trapeze‐flap plasty, percutaneous collagen induction, phototherapy and fractional CO2 laser—demonstrate a relatively good response and an acceptable safety profile. Notably, light‐based therapies/lasers may serve as safe, effective and tolerable options for scar treatment in this age group, often eliminating the need for general anaesthesia.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".