EXPRESS—Examining Pediatric Resuscitation Education Using Simulation and Scripting
Bibliographic record
Abstract
Over the past decade, medical simulation has evolved into an essential component of pediatric resuscitation education and team training. Evidence to support its value as an adjunct to traditional methods of education is expanding; however, large multicenter studies are very rare. Simulation-based researchers currently face many challenges related to small sample sizes, poor generalizability, and paucity of clinically proven and relevant outcome measures. The Examining Pediatric Resuscitation Education Using Simulation and Scripting (EXPRESS) pediatric simulation research collaborative was formed in an attempt to directly address and overcome these challenges. The primary mission of the EXPRESS collaborative is to improve the delivery of medical care to critically ill children by answering important research questions pertaining to pediatric resuscitation and education and is focused on using simulation either as a key intervention of interest or as the outcome measurement tool. Going forward, the collaborative aims to expand its membership internationally and collectively identify pediatric resuscitation and simulation-based research priorities and use these to guide future projects. Ultimately, we hope that with innovative and high-quality research, the EXPRESS pediatric simulation research collaborative will help to build momentum for simulation-based research on an international level.
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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 | 0.023 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".