Enhancing the Community Chain of Survival: A Simulation Study of Defibrillator Delivery by Food Delivery Riders in a High-Density City
Bibliographic record
Abstract
BACKGROUND: Timely defibrillator delivery for out-of-hospital cardiac arrests (OHCAs) remains challenging, with most cases relying on emergency medical services (EMSs) for response. In this simulation study we examined the feasibility of using food delivery (FD) scooter riders as first responders for defibrillator delivery in OHCA incidents and compared simulated defibrillator arrival times with documented times. METHODS: This simulation study was conducted in Taipei, a densely populated city with a high concentration of scooter-based FD riders. OHCA cases were retrieved from the OHCA Registry of the Taipei City Fire Department between 2017 and 2019, public defibrillator locations and operating hours were sourced, and FD hotspots were identified using the Uber Eats platform. The simulation assumed that every open restaurant in a hotspot had exactly 1 FD rider waiting and ready to respond to OHCAs within a 2-km radius. Response rates of FD riders were varied, and both simulated and documented defibrillator arrival times were analyzed. Differences in defibrillator arrival times during peak and off-peak hours were also assessed. RESULTS: With a 10% FD rider response rate, the defibrillator arrival time decreased by 2.99 minutes, representing approximately 44% of the original EMS response time. In the simulation, over 60% of OHCAs were successfully attended. Achieving 80% coverage during peak hours required 13.4% of FD riders to respond. CONCLUSIONS: Integrating FD riders into the EMS system could reduce defibrillator arrival times, decreasing patient waiting time for defibrillation. This approach is particularly effective during peak hours, when a higher proportion of OHCAs can be addressed.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".