Regional Variation in Out-of-Hospital Cardiac Arrest Survival in the United States
Bibliographic record
Abstract
BACKGROUND: Although previous studies have shown marked variation in out-of-hospital cardiac arrest survival across US regions, factors underlying this survival variation remain incompletely explained. METHODS AND RESULTS: Using data from the Cardiac Arrest Registry to Enhance Survival, we identified 96 662 adult patients with out-of-hospital cardiac arrest in 132 US counties. We used hierarchical regression models to examine county-level variation in rates of survival and survival with functional recovery (defined as Cerebral Performance Category score of 1 or 2) and examined the contribution of demographics, cardiac arrest characteristics, bystander cardiopulmonary resuscitation, automated external defibrillator use, and county-level sociodemographic factors in survival variation across counties. A total of 9317 (9.6%) patients survived to discharge, and 7176 (7.4%) achieved functional recovery. At a county level, there was marked variation in rates of survival to discharge (range, 3.4%-22.0%; median odds ratio, 1.40; 95% confidence interval, 1.32-1.46) and survival with functional recovery (range, 0.8%-21.0%; median odds ratio, 1.53; 95% confidence interval, 1.43-1.62). County-level rates of bystander cardiopulmonary resuscitation and automated external defibrillator use were positively correlated with both outcomes (P<0.0001 for all). Patient demographic and cardiac arrest characteristics explained 4.8% and 27.7% of the county-level variation in survival, respectively. Additional adjustment of bystander cardiopulmonary resuscitation and automated external defibrillator explained 41% of the survival variation, and this increased to 50.4% after adjustment of county-level sociodemographic factors. Similar findings were noted in analyses of survival with functional recovery. CONCLUSIONS: Although out-of-hospital cardiac arrest survival varies significantly across US counties, a substantial proportion of the variation is attributable to differences in bystander response across communities.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".