Fractional CO2 Laser for Pediatric Hypertrophic Scars: Lessons Learned from a Prematurely Terminated Split-Scar Trial
Bibliographic record
Abstract
Background: Assessing hypertrophic scar (HTS) interventions is challenging because scars continue to undergo dynamic changes. A split-scar design can distinguish treatment effects from natural HTS evolution. Despite promising reports of ablative fractional CO2 lasers (AFCO2Ls) for HTS, split-scar evidence, particularly in pediatric scars, remains limited. Objective: To explore the feasibility of a split-scar design in assessing AFCO2L’s impact on pediatric HTS and to identify potential trends in treatment outcomes. Methods: Initially designed as a prospective single-center split-scar randomized controlled trial, our study transitioned to a feasibility trial due to recruitment challenges. Pediatric patients aged 1–17 years with HTS suitable for split-scar evaluation received three AFCO2L treatments at 6–8-week intervals, with outcomes assessed using the Vancouver Scar Scale (VSS), SCAR-Q, and Cutometer. Results: Recruitment was limited by COVID-19 restrictions, concerns about general anesthesia for split-scar treatment, and low interest in divided-scar interventions, resulting in only 6 participants with 9 scars enrolled, far below the target sample size of 44. This small heterogeneous sample precluded meaningful clinical outcome analysis. Conclusions: Our feasibility trial highlights challenges in conducting rigorous pediatric HTS studies and the need for careful interpretation of evidence due to potential publication bias. Future trials should focus on tailored recruitment and comprehensive reporting to improve feasibility and reliability.
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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 | 0.103 | 0.159 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| 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".