Acquisition and Maintenance of Expertise on Burn Patient Management in the Pediatric Intensive Care Unit
Bibliographic record
Abstract
Critically ill burn patients account for less than 0.25% of all patients admitted to the pediatric intensive care unit (PICU) at our institution.Most pediatric burn patients do not require critical care; those who do typically have larger total body surface area burns in addition to the involvement of multiple other organ systems (1).The mortality rate for children with .60%total body surface area burns is between 14% and 35% (2).It is therefore imperative for PICU clinicians to find ways to maintain competence for this population that represents a low-frequency and high-stakes condition.In this commentary, we share our expertise in having designed a curriculum that addresses maintenance of competence in burn management for the interprofessional team in PICU.Reports dedicated to burn education are focused on medical students, emergency medicine, and plastic surgery clinicians and mainly employ a strategy of lectures and journal clubs (3-9).Only one study describes using simulation in the form of standardized actors wearing moulage and a burn suit for training multiprofessional plastic surgery teams (10, 11).Overall, the existing burn education literature has focused primarily on skill acquisition with minimal guidance regarding how best to (
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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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 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".