Frostbite in the Pediatric Population
Bibliographic record
Abstract
INTRODUCTION: Frostbite in the pediatric population, where skeletal maturity has not been achieved, can have important repercussions on subsequent growth. Yet, the optimal management of frostbite injuries in children remains vague. This review aims to summarize the current evidence for frostbite management in children and understand Canadian practice trends on this topic. METHODS: A review using Medline, Scopus, Web of Science, and gray literature was performed to identify relevant literature on the clinical manifestations, diagnostic methods, and treatment options in pediatric frostbite. An online survey was sent to plastic surgeons through the Canadian Society of Plastic Surgeons (CSPS) mailing list to further identify national practices and trends for pediatric frostbite management. RESULTS: A total of 109 articles were reviewed. No article provided a specific algorithm for pediatric frostbite, with existing recommendations suggesting the use of adult guidelines for treating children. Our survey yielded 9 responses and highlighted the rarity of pediatric frostbite cases, with no responder treating more than 10 cases per year. Most (55.6%) do not use a pediatric-specific treatment algorithm, whereas 30% apply adult guidelines. A conservative approach focusing on rewarming (55.6%), limb elevation (50%), and tetanus status verification (66.7%) was predominant. Imaging and surgical interventions seem to be reserved for severe cases. CONCLUSIONS: The current literature for pediatric frostbite management lacks specificity. Canadian practices vary, with a trend toward a conservative approach. The limited evidence and rarity of experience highlight the need for further research, ideally in a collaborative multicentric manner, to create a consensus for pediatric frostbite care.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".