Abstract 244: Accelerating AED Use In Out-of-Hospital Cardiac Arrest With Combinations Of First Responder And Drone Delivery To Achieve 5-minute AED Deployment
Bibliographic record
Abstract
Background: Defibrillation in the critical first minutes of out-of-hospital cardiac arrests (OHCAs) can significantly improve survival rates but getting timely access to AEDs is a major barrier. We estimated the potential of a statewide program for drone-delivered AEDs in North Carolina (NC) when integrated into EMS response that includes police and fire first responders (FR). Methods: Using CARES registry data, we included all patients >18 years with OHCA occurring between 1/1/2013-12/31/2019 in 44 NC counties. We determined the improvement in AED arrival time by providing AEDs to all FR, who routinely arrive earlier than EMS but currently may not have an AED. We developed mathematical models to place drones within each county to achieve 5-min AED arrival times for 75% of OHCAs in each county. We predicted ambulance response times and resulting drone dispatch decisions using linear regression. Models were trained using 2013-2018 OHCA data and were validated with 2019 data. Mean response times were compared with historical response using the Wilcoxon signed-rank test. Results: We identified 27,199 OHCAs with historical median county-level response times of 6.7 [IQR: 5.9-7.5 mins]. Providing all FR with AEDs reduced response times to 5.9 [IQR: 5.3-6.9] mins and increased OHCAs with AED arrival times of <5 min to 35.6% [IQR: 26.1-43.4%]. Mathematical models estimated that 273 optimally located drones across the 44 counties would reduce response times for out-of-sample OHCAs to 3.7 [IQR: 3.4-3.9] mins and increase OHCAs with AED arrival times of <5 min to 73.0% [IQR: 65.4-78.8%]. The drone network improved response times (p<0.05) compared to historical response times in 43 out of 44 counties. Conclusion: Full deployment of AEDs by FR plus optimized drone AED delivery was estimated to significantly improve response times based on historical patterns of OHCA in NC. The number of drones and their impact varies across counties based on OHCA incidence rates and population density.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".