Challenges & barriers for real-time integration of drones in emergency cardiac care: Lessons from the United States, Sweden, & Canada
Bibliographic record
Abstract
Importance: Out-of-hospital cardiac arrest (OHCA) is a leading cause of morbidity and mortality in the US and Europe (∼600,000 incident events annually) and around the world (∼3.8 million). With every minute that passes without cardiopulmonary resuscitation or defibrillation, the probability of survival decreases by 10%. Preliminary studies suggest that uncrewed aircraft systems, also known as drones, can deliver automated external defibrillators (AEDs) to OHCA victims faster than ground transport and potentially save lives. Objective: To date, the United States (US), Sweden, and Canada have made significant contributions to the knowledge base regarding AED-equipped drones. The purpose of this Special Communication is to explore the challenges and facilitators impacting the progress of AED-equipped drone integration into emergency medicine research and applications in the US, Sweden, and Canada. We also explore opportunities to propel this innovative and important research forward. Evidence review: In this narrative review, we summarize the AED-drone research to date from the US, Sweden, and Canada, including the first drone-assisted delivery of an AED to an OHCA. Further, we compare the research environment, emergency medical systems, and aviation regulatory environment in each country as they apply to OHCA, AEDs, and drones. Finally, we provide recommendations for advancing research and implementation of AED-drone technology into emergency care. Findings: The rates that drone technologies have been integrated into both research and real-life emergency care in each country varies considerably. Based on current research, there is significant potential in incorporating AED-equipped drones into the chain of survival for OHCA emergency response. Comparing the different environments and systems in each country revealed ways that each can serve as a facilitator or barrier to future AED-drone research. Conclusions and relevance: The US, Sweden, and Canada each offers different challenges and opportunities in this field of research. Together, the international community can learn from one another to optimize integration of AED-equipped drones into emergency systems of care.
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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 | 0.010 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.014 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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".