Abstract 147: AED on the Fly: A Drone Delivery Feasibility Study for Rural and Remote Out-Of-Hospital Cardiac Arrest
Bibliographic record
Abstract
Introduction: Time-to-treatment plays a pivotal role in survival from sudden cardiac arrest (SCA). Every minute of delay in defibrillation results in a 7-10% reduction in survival. Time to defibrillation is particularly problematic in rural and remote regions, where traditional bystander and EMS response is often prolonged and automated external defibrillators (AED) are often not available. The objective of this study was to examine the feasibility of a novel AED drone delivery method for rural and remote SCA. A secondary objective was to compare times between AED drone delivery and ambulance response to various mock SCA resuscitations. Methods: We conducted four simulations to determine the feasibility of AED drone delivery to mock SCA resuscitations in a rural public setting in Ontario, Canada. During each simulation, a “mock” call was placed to 911 and a single AED drone and an ambulance were simultaneously dispatched from the same location to a pre-determined destination for a mock SCA. Once on scene, trained first responders retrieved the AED from the drone and initiated resuscitative efforts on a manikin until paramedics arrived. Results: The distance from dispatch location to scene varied from 6.6 kms to 8.8 kms. Mean (SD) response time from 911 call to arrive at mock code location was 11.2 (1.0) for EMS compared to 8.1 (0.1) minutes for AED drone delivery. In all four simulations, the AED drone arrived before EMS, ranging from 2.1 minutes to 4.4 minutes faster. Mean (SD) time from AED removal from drone to application to manikin by a trained responder was 35(5) sec. No difficulties were encountered in drone activation by dispatch, drone lift off, landing or removal of the AED from the drone by responders. Conclusions: This implementation study suggests AED drone delivery is feasible with improvements in response time during a simulated SCA scenario. These results suggest the potential for AED drone delivery to decrease time to first defibrillation in rural and remote communities. Further research is required to determine the appropriate distance for drone delivery of an AED in an integrated EMS system as well as optimal strategies to simplify bystander application of a drone delivered AED.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".