The Managing Emergencies in Paediatric Anaesthesia global rating scale is a reliable tool for simulation‐based assessment in pediatric anesthesia crisis management
Bibliographic record
Abstract
INTRODUCTION: The use of simulation-based assessments for high-stakes physician examinations remains controversial. The Managing Emergencies in Paediatric Anaesthesia course uses simulation to teach evidence-based management of anesthesia crises to trainee anesthetists in the United Kingdom (UK) and Canada. In this study, we investigated the feasibility and reliability of custom-designed scenario-specific performance checklists and a global rating scale (GRS) assessing readiness for independent practice. METHODS: After research ethics board approval, subjects were videoed managing simulated pediatric anesthesia crises in a single Canadian teaching hospital. Each subject was randomized to two of six different scenarios. All 60 scenarios were subsequently rated by four blinded raters (two in the UK, two in Canada) using the checklists and GRS. The actual and predicted reliability of the tools was calculated for different numbers of raters using the intraclass correlation coefficient (ICC) and the Spearman-Brown prophecy formula. RESULTS: Average measures ICCs ranged from 'substantial' to 'near perfect' (P ≤ 0.001). The reliability of the checklists and the GRS was similar. Single measures ICCs showed more variability than average measures ICC. At least two raters would be required to achieve acceptable reliability. CONCLUSIONS: We have established the reliability of a GRS to assess the management of simulated crisis scenarios in pediatric anesthesia, and this tool is feasible within the setting of a research study. The global rating scale allows raters to make a judgement regarding a participant's readiness for independent practice. These tools may be used in the future research examining simulation-based assessment.
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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 | 0.014 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".