279 Drone-delivered automated external defibrillators for out-of-hospital cardiac arrest. a scoping review
Bibliographic record
Abstract
Background Drone-delivery of automated external defibrillators (AEDs) to out-of-hospital cardiac arrest (OHCA) is increasingly being investigated for early defibrillation. To obtain an overview of international status and feasibility, we performed a scoping review of the literature concerning drone-AED delivery. Method Combining search strings of drone with OHCA OR AED in ‘MESH’ and ‘text-word’ searches and with synonyms, Embase and PubMed was searched on 29th of December 2021. Peer-reviewed articles, abstracts, editorials, and letters published in English language were included. Results After duplicate removal, title/abstract screening, and full-text review, a total of 23/122 records were included. Included studies were either test-flights with drone-AED or virtual flight models calculating drone-AED coverage in different ways. Fifteen studies (from Sweden, Canada, USA (Washington, Virginia, North Carolina, and Utah) France, Germany, Northern Ireland, South Korea, and Austria) concerned location and quantity of drone bases in a virtual drone-flight simulation model. All studies estimated an overall time gain to AED on scene compared with standard Emergency Medical Service (EMS) arrival, with varying proportions of OHCAs covered by drone-AED delivery prior to standard EMS. Seven studies concerned simulation flights, 4 of these included the human-drone interaction. One study delivered AEDs to real-life suspected OHCA with a delivery success rate of 92%. All these studies found drone-delivery of AEDs feasible. Conclusion All 23 investigative studies found drone-delivery of AEDs to suspected OHCA feasible and with an overall estimated time gain compared with standard EMS. Only one study described drone-AED delivery to real-life suspected OHCA. Conflict of interest None. Funding Novo Nordisk Foundation.
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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 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.016 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".