A Single Institution’s Surgical Care Model for Pediatric Burns With ≤10% Body Surface Area Involvement
Bibliographic record
Abstract
Small burn injuries are extremely prevalent in the pediatric population and continue to pose a challenge for clinicians. Despite their high incidence, a standardized algorithm for treating small burns does not currently exist, and care is often guided by clinical judgement and resource availability. The aim of this study was to explore the utility of a two-stage grafting technique, involving allograft and autograft, for treating small burns (≤10% total body surface area) in pediatric patients. A retrospective review of patients aged 0-18 years who had a small burn and underwent a two-stage grafting procedure between September 1, 2018 and September 1, 2022 was conducted. One hundred and seventy-five patients with 220 wounds met the inclusion criteria for this study. The mean time from presentation to allograft surgery was 11.4 days (SD 5.2) followed by autograft surgery approximately one week later. Most patients were discharged within 24 hours following allograft surgery (87.4%) and autograft surgery (81.1%). Mean autograft take was 97.7% (SD 11.8) with only four patients experiencing significant graft loss requiring subsequent re-grafting. These positive outcomes demonstrate that the two-stage technique can be successfully utilized for treating smaller pediatric burns. Moreover, these findings help to address the significant knowledge gap regarding the optimal approach to treating small burn wounds. Further research is warranted to learn more about aesthetic outcomes following two-stage grafting and determine how it compares to other techniques for treating small burns.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".