The impact of drone delivery of an automated external defibrillator: a simulation feasibility study
Bibliographic record
Abstract
Introduction: Out-of-hospital cardiac arrest (OHCA) is a leading cause of death in Europe. Early defibrillation is associated with improved outcomes. While this may be delivered by members of the public using an automated external defibrillator (AED), they are used infrequently. Drone delivery of an AED may enable quicker defibrillation compared to awaiting arrival of emergency medical services. Little is known about how members of the public may react to AED delivery or about the potential impact of retrieving an AED on the provision of high-quality cardiopulmonary resuscitation (CPR). Methods: A feasibility study using a simulated OHCA scenario was completed by members of the public. Participants performed CPR on a manikin, guided by an ambulance service call handler, which was interrupted by AED delivery. CPR quality and the duration of the interruption for AED retrieval were recorded, and participants' feedback on the scenario was collected using a survey. Results: Twelve participants completed the study. Overall, a median of 61% (interquartile range [IQR] 21-79) of chest compressions were delivered at the correct speed, and 99% (IQR 78-100) at the correct depth. CPR was discontinued for a median of 116 (96-135) seconds to retrieve an AED and deliver a shock. Participants described the scenario as stressful and challenging, were supportive of the concept of AED delivery by drone and emphasised the value of call-handler instructions and guidance. Conclusion: This study demonstrated the feasibility of a process and outcomes evaluation of simulated drone-delivered AED to members of the public. The retrieval process was associated with notable interruption in the delivery of CPR, but it remains unknown whether any impact of this may be offset by expedited use of the AED. Understanding the likely overall impact of drone delivery of AEDs on patient outcomes is critical before this approach should be considered in clinical practice.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".