A hospital mass casualty exercise using city buses and a tent as a hybrid system for patient decontamination
Bibliographic record
Abstract
OBJECTIVE: A hospital mass casualty simulation exercise testing feasibility of two city buses and a tent as a hybrid system for patient decontamination. DESIGN: Observational study of a single mass casualty simulation exercise involving patient decontamination SETTING: Held on May 26, 2016 at the Montreal General Hospital, a Level 1 Trauma center without a garage. PATIENTS, PARTICIPANTS: Twenty-one medical staff including nurses, doctors, and patient attendants, and 30 volunteer-simulated patients. INTERVENTIONS: The foregrounds of the hospital were cordoned off to create a single-entry point for the simulated patients that were identified as contaminated (C) by staff wearing personal protective equipment. Non-contaminated patients were directed to a separate hospital entrance. C patients were triaged in Bus 1 to determine priority for decontamination. Bus 2 served as a holding area for stable patients awaiting decontamination. Patients were decontaminated in appropriate tent sections (non-ambulatory, ambulatory male or female) and then directed to the emergency department. RESULTS: Direct observation and participant feedback suggested that buses may provide adequate shelter for C patients. However, buses had limited capacity for non-ambulatory patients, who were not easily transported inside. Furthermore, areas of improvement were identified in communication, staffing, equipment, and coordination of operations. CONCLUSIONS: The use of city buses as triage and waiting zones prior to decontamination appears feasible for centers without a garage and facing unpredictable weather conditions. Further simulations are required for fine-tuning and testing real-time unfolding of tasks, ideally during an unannounced exercise.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".