Analysis of Military–Civilian Patient Handoff at Vista Forge Multi-Agency Nuclear Disaster Exercise 2022
Bibliographic record
Abstract
INTRODUCTION: The leadership of Vista Forge 2022 requested evaluation of the handoff process between military assets and civilian emergency medical services (EMS) providers by the Beth Israel Deaconess Fellowship in Disaster Medicine (BIDMF). Vista Forge was a multi-agency military-civilian full-scale disaster exercise coordinated by the U.S. Military. The exercise, held in Atlanta, Georgia, simulated response to a nuclear bomb in an urban setting by military and civilian disaster teams. MATERIALS AND METHODS: BIDMF had several two-person teams who monitored handoff procedures between military assets after decontamination and civilian emergency medical services providers during the exercise evaluation. RESULTS: A verbal handoff between military and civilian entities was usually not done. Triage tags placed on mannequins before decontamination remained attached to the bodies and were sent with them to civilian hospitals. Triage tags were generic military forms without specific radiation or chemical exposure information. Not all decontamination groups had the same medical capabilities, and in a disaster it is unclear how these teams would manage medical emergencies. CONCLUSIONS: Future studies should develop a standardized handoff procedure to be used in mass casualty situations, and trial it in future multi-agency disaster exercises. Radiation specific triage tags should be considered.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".