Integration of In Situ Simulation Into an Emergency Department Code Orange Exercise in a Tertiary Care Trauma Referral Center
Bibliographic record
Abstract
OBJECTIVES: Disaster-preparedness and response are a commonly overlooked aspect of hospital policy and can frequently be outdated and undertested. Simulation-based education has become a core education modality within Canadian medical training programs. We hypothesized that integrating in situ simulation (ISS) into a hospital-wide, mass-casualty response exercise would enhance realism and our ability to identify latent safety threats (LSTs). METHODS: Using ISS we created a simulated mass shooting scenario with 20 patients, played by actors in full moulage, presenting to a large tertiary care hospital over a 50-minute period. RESULTS: Integrating ISS into our exercise created a realistic experience for the participants involved and improved participant education, while imparting enough systemic stress to expose LSTs associated within patient care and hospital policy. CONCLUSION: Overall, ISS was successfully used and enhanced a large-scale test of our hospital's mass-casualty response plan.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".