Abstract 105: Rural-Urban Differences for Integrated First Responder and Drone Automated External Defibrillator Delivery in North Carolina
Bibliographic record
Abstract
Background: Early defibrillation for out-of-hospital cardiac arrest (OHCA) can improve survival significantly, but timely access to AEDs remains a barrier especially in rural areas. We analyzed whether differences exist between urban and rural areas for a proposed program of first responder (FR) and drone AED delivery in North Carolina. Methods: Using CARES registry data, we identified OHCAs in 48 counties between Jan. 2013-Dec. 2019. We applied a hypothetical intervention in which 1) all FRs were provided AEDs and 2) a network of AED-carrying drones was optimized within each county to maximize <5-minute response times (defined as the interval from 9-1-1 call receipt to arrival of an AED via EMS, FR, or drone). Within each county, we classified census tracts by rural population with <25% as urban, 25-75% as mixed, and >75% as rural. We included only counties with ≥10 OHCAs per year in both rural and urban census tracts to compare resource allocation for counties with large geographic variation. Historical and intervention response times stratified by rurality were compared via the Wilcoxon signed-rank test. Results: We included 19 counties and 8,955 OHCAs (5,754 urban, 3,201 rural). The historical median response time was 6.9 mins [IQR: 5.1-8.7] in urban census tracts and 9.4 mins [IQR: 7.0-12.1] in rural tracts. The FR + drone intervention reduced estimated median response times by 42% to 4.0 mins [IQR: 3.1-5.0] in urban areas and by 24% to 7.1 mins [IQR: 5.2-9.4] in rural areas. Five-minute coverage improved from 24% to 77% for urban areas and 10% to 23% for rural areas. All counties showed significant improvement in response time for both urban and rural populations (p<0.01). Conclusion: Deployment of AEDs by FR and drones optimized for 5-minute coverage was estimated to improve AED response more so in urban areas than rural areas. Other optimization parameters are needed to reduce inequality between urban and rural response times in areas with large geographic variation.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".