A Systematic Review of Effectiveness of Automated External Defibrillators Delivered by Drones
Bibliographic record
Abstract
BACKGROUND: According to the 2019 annual report by Fire and Disaster Management Agency (FDMA) in Japan, the survival rate of patients with Out-of-Hospital cardiac arrest (OHCA) who were rescued by Automated External Defibrillators (AED) was 6.2 times higher than those who were not treated appropriately. Unmanned Aerial Vehicles (UAV) have been evaluated as the means of delivering medical equipment and goods. This study was therefore designed to evaluate the effectiveness of UAV technology applied to AED delivery through a systematic review methodology. METHODS: Preferred Reporting Items for Systematic Reviews and Meta- Analyses (PRISMA) statement was utilized to guide the review. Electronic databases such as Medline through PubMed and CiNii were searched. Search terms were used in a variety of combinations, including AED, UAV, and drone in English and Japanese. RESULTS: Nine articles were identified through the review process. Most of the studies were conducted in Western countries, and all of them were done after 2016. Seven studies evaluated the time reductions in the delivery of the defibrillation in OHCAs by simulation study methods and/or test flights of UAV. All the studies showed the positive results regarding the time reductions to AED access by bystanders compared with the current setting of no UAV networks. CONCLUSION: The studies included in this review showed UAV technology around AED delivery would have the potential to reduce the time of the defibrillation in OHCA patients. More evidence especially around the real-world utilization and the cost-effectiveness of the technology deployment are expected for the future adaptation.
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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 | 0.011 | 0.052 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.010 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".