Influence of scar age, laser type and laser treatment intervals on paediatric burn scars: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Background Laser therapy has emerged to play a valuable role in the treatment of paediatric burn scars; however, there is heterogeneity in the literature, particularly concerning optimal timing for initiation of laser therapy. This study aims to investigate the effect of factors such as scar age, type of laser and laser treatment interval on burn scar outcomes in children by meta-analysis of previous studies. Methods A literature search was conducted across seven databases in May 2022 to understand the effects of laser therapy on burn scar outcomes in paediatric patients by metanalysis of standardized mean difference (SMD) between pre- and post-laser intervention. Meta-analyses were performed using the Comprehensive Meta-Analysis software version 4.0. Fixed models were selected when there was no significant heterogeneity, and the random effects model was selected for analysis when significant heterogeneity was identified. For all analyses, a p-value < 0.05 was considered significant. Results Seven studies were included in the meta-analysis with a total of 467 patients. Laser therapy significantly improved Vancouver Scar Scale (VSS)/Total Patient and Observer Scar Assessment Scale (Total POSAS), vascularity, pliability, pigmentation and scar height of burn scars. Significant heterogeneity was found between the studies and thus subgroup analyses were performed. Early laser therapy (<12 months post-injury) significantly improved VSS/POSAS scores compared to latent therapy (>12 months post-injury) {SMD −1.97 [95% confidence interval (CI) = −3.08; −0.87], p < 0.001 vs −0.59 [95%CI = −1.10; −0.07], p = 0.03} as well as vascularity {SMD −3.95 [95%CI = −4.38; −3.53], p < 0.001 vs −0.48 [95%CI = −0.66; −0.30], p < 0.001}. Non-ablative laser was most effective, significantly reducing VSS/POSAS, vascularity, pliability and scar height outcomes compared to ablative, pulse dye laser and a combination of ablative and pulse dye laser. Shorter treatment intervals of <4 weeks significantly reduced VSS/POSAS and scar height outcomes compared to intervals of 4 to 6 weeks. Conclusions Efficacy of laser therapy in the paediatric population is influenced by scar age, type of laser and interval between laser therapy application. The result of this study particularly challenges the currently accepted initiation time for laser treatment. Significant heterogeneity was observed within the studies, which suggests the need to explore other confounding factors influencing burn scar outcomes after laser therapy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.010 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.000 | 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 teacher head, 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".