Association Between Driving Distance From Nearest Fire Station and Survival of Out‐of‐Hospital Cardiac Arrest
Bibliographic record
Abstract
Background Firefighter first responders dispatched in parallel with emergency medical services ( EMS ) personnel for out-of-hospital cardiac arrests ( OHCA ) can provide early defibrillation to improve survival. We examined whether survival following first responder defibrillation differed according to driving distance from nearest fire station to OHCA site. Methods and Results From the CARES (Cardiac Arrest Registry to Enhance Survival) registry, we identified non- EMS witnessed OHCA s of presumed cardiac cause from 2010 to 2014 in Durham, Mecklenburg, and Wake counties, North Carolina. We used logistic regression to estimate the association between calculated driving distances (≤1, 1-1.5, 1.5-2, and >2 miles) and survival to hospital discharge following first responder defibrillation compared with defibrillation by EMS personnel. In total, 5020 OHCA s were included in the study. First responders more often applied the first automated external defibrillators at the shortest distances (≤1 mile) versus longest distances (>2 miles) (53.4% versus 46.6%, respectively, P<0.001). When compared with EMS defibrillation, first responder defibrillation within 1 mile and 1 to 1.5 miles of the nearest fire station was associated with increased survival to hospital discharge (odds ratio 2.01 [95% confidence interval 1.46-2.78] and odds ratio 1.61 [95% confidence interval 1.10-2.35], respectively). However, at the longest distances (1.5-2.0 and >2.0 miles), survival following first responder defibrillation did not differ from EMS defibrillation (odds ratio 0.77 [95% confidence interval 0.48-1.21] and odds ratio 0.97 [95% confidence interval 0.67-1.41], respectively). Conclusions Shorter driving distance from nearest fire station to OHCA location was associated with improved survival following defibrillation by first responders. These results suggest that the location of first responder units should be considered when organizing prehospital systems of OHCA care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".