Abstract 9873: Comparing Base Locations for Drone-Delivered Defibrillators
Bibliographic record
Abstract
Introduction: Drone-delivered defibrillators may improve response for out-of-hospital cardiac arrest (OHCA). Prior studies have assumed that drones may be stationed at any police, fire, or paramedic station; however, cross-service implementation may not be logistically feasible. We sought to compare estimated response times by drone base location type. Methods: We included OHCAs (Jan. 2014 to Dec. 2020) in southern Vancouver Island, British Columbia, Canada where OHCA response includes fire and paramedic services. We created four models with candidate drone base locations at: police stations, fire stations, paramedic stations, and on a grid with 1 km sides as an optimistic model. We used mathematical optimization to select 1-5 drone bases for each model. Assuming a drone system had been in place during the study period and accounting for drone availability, we estimated 9-1-1 call-to-defibrillator intervals (measured to either drone, paramedic, or fire arrival) and calculated the proportion of OHCAs where a drone would arrive prior to fire and paramedic for each model. Median response times were compared to historical response using one-sided sign tests. Results: We included 1,610 OHCAs with a median historical response time of 6.4 mins (IQR 5.0-8.6). We identified 21 police stations, 59 fire stations, 21 paramedic stations, and 7,008 grid locations in the study area. Median 9-1-1 call-to-defibrillator intervals ranged from 4.3-5.3 mins for police, 4.3-5.3 mins for fire, 4.5-5.4 mins for paramedic, and 4.2-5.4 mins for grid locations (all P<0.001). Drones arrived prior to fire and paramedics in 36.6-65.4% of cases for police, 38.1-66.2% for fire, 37.3-63.2% for paramedic, and 35.7-66.8% for grid locations. Conclusion: Locating drone bases at different types of emergency service stations significantly decreases 9-1-1 call-to-defibrillator intervals, while resulting in similar response intervals to those achieved using optimistic grid-optimal locations.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".