Volunteer First Responders for Optimizing Management of Mass Casualty Incidents
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 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 it