257 Reduction in EMS response times for out-of-hospital cardiac arrest using drone-like flying ambulances in large urban areas in France and Canada: An international, quasi-experimental study
Bibliographic record
Abstract
Background Shortening EMS response times lead to better outcomes after out-of-hospital cardiac arrest (OHCA). To overcome constraints encountered by ground ambulances, vertical take-off and landing (VTOL) capable flying ambulances are currently being developed. We compared simulated VTOL response to historical ground ambulance response for OHCAs in two large metropolitan areas in Europe and North America. Method We conducted an international, multicenter, quasi-experimental study on adult, non-traumatic, EMS-assessed, non-EMS witnessed OHCA occurring in the greater Paris (France) and Vancouver (Canada) metropolitan areas, over a 2-year span (2018–2020). Data were drawn from Utstein-style, population-based OHCA registries. VTOL response times were simulated based on prototype specifications. Response times were defined from call reception to arrival at scene. Simulation models considered 1–5 VTOL vehicles placed in optimized locations. We determined the proportion of OHCAs for which VTOL response times were at least 1-min shorter than historical response from ground-based units. Results In total, 13,933 cases were included (6,616 in Paris; 7,317 in Vancouver). Simulated VTOL response times were substantially shorter than those of ground-based units, varying from 59% (1 VTOL) to 76% (5 VTOL) in Paris, and 17% (1 VTOL) to 40% (5 VTOL) in Vancouver. In both locations, median response times were reduced by 1–3 minutes, and 90th percentile response times by 1–5 minutes, varying upon model configuration. For OHCAs with improved response, the median improvement was 3–4 minutes, and 90th percentile improvement was 8–10 minutes in both areas. Conclusion Simulation models of VTOL-capable flying ambulances show major theoretical reduction in EMS response times for OHCAs in two large European and North American metropolitan areas. Conflict of interest None. Funding None.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".