The Reliability of the Resuscitation Assessment Tool (RAT) in Assessing Emergency Medicine Resident Competence in Pediatric Resuscitation Scenarios: A Prospective Observational Pilot Study
Bibliographic record
Abstract
Introduction Emergency medicine (EM) postgraduate medical education in Canada has transitioned from traditional time-based training to competency-based medical education (CBME). In order to promote residents through stages of training, simulated assessments are needed to evaluate residents in high-stakes but low-frequency medical emergencies. There remains a gap in the literature pertaining to the use of evaluative tools in simulation, such as the Resuscitation Assessment Tool (RAT) in the new CBME curriculum design. Methods We completed a pilot study of resident physicians in one Canadian EM training program to evaluate the effectiveness and reliability of a simulation-based RAT for pediatric resuscitation. We recorded 10 EM trainees completing simulated scenarios and had nine EM physicians use the RAT tool to evaluate their performances. Generalizability theory was used to evaluate the reliability of the RAT tool. Results The mean RAT score for the management of pediatric myocarditis, cardiac arrest, and septic shock (appendicitis) across raters was 3.70, 3.73, and 4.50, respectively. The overall generalizability coefficient for testing simulated pediatric performance competency was 0.77 for internal consistency and 0.75 for absolute agreement. The performance of senior participants was superior to that of junior participants in the management of pediatric myocarditis (p = 0.01) but not statistically significant in the management of pediatric septic shock (p=0.77) or cardiac arrest (p =0.61). Conclusion Overall, our findings suggest that with an appropriately chosen simulated scenario, the RAT tool can be used effectively for the simulation of high-stakes and low-frequency scenarios for practice to enhance the new CBME curriculum in emergency medicine training programs.
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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 | 0.025 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".