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Disaster-preparedness and response are a commonly overlooked aspect of hospital policy and can frequently be outdated and undertested.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 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".