Volunteer First Responders for Optimizing Management of Mass Casualty Incidents
Bibliographic record
Abstract
OBJECTIVE: Rapid response to a trauma incident is vital for saving lives. However, in a mass casualty incident (MCI), there may not be enough resources (first responders and equipment) to adequately triage, prepare, and evacuate every injured person. To address this deficit, a Volunteer First Responder (VFR) program was established. METHODS: This paper describes the organizational structure and roles of the VFR program, outlines the geographical distribution of volunteers, and evaluates response times to 3 MCIs for both ambulance services and VFRs in 2000 and 2016. RESULTS: When mapped, the spatial distribution of VFRs and ambulance stations closely and deliberately reflects the population distribution of Israel. We found that VFRs were consistently first to arrive at the scene of an MCI and greatly increased the number of personnel available to assist with MCI management in urban, suburban, and rural settings. CONCLUSIONS: The VFR program provides an important and effective life-saving resource to supplement emergency first response. Given the known importance of rapid response to trauma, VFRs likely contribute to reduced trauma mortality, although further research is needed in order to examine this question specifically. (Disaster Med Public Health Preparedness. 2019;13:287-294).
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.004 |
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".