Urban-Rural Differences in Cardiac Arrest Outcomes: A Retrospective Population-Based Cohort Study
Bibliographic record
Abstract
Background Approximately 10% of people who suffer an out-of-hospital cardiac arrest (OHCA) treated by paramedics survive to hospital discharge. Survival differs by up to 19.2% between urban centres and rural areas. Our goal was to investigate the differences in OHCA survival between urban centres and rural areas. Methods This was a retrospective cohort study of OHCA patients treated by Nova Scotia Emergency Medical Services (EMS) in 2017. Cases of traumatic, expected, and noncardiac OHCA were excluded. Data were collected from the Emergency Health Service electronic patient care record system and the discharge abstract database. Geographic information system analysis classified cases as being in urban centres (population > 1000 people) or rural areas, using 2016 Canadian Census boundaries. The primary outcome was survival to hospital discharge. Multivariable logistic regression covariates were age, sex, bystander resuscitation, whether the arrest was witnessed, public location, and preceding symptoms. Results A total of 510 OHCAs treated by Nova Scotia Emergency Medical Services were included for analysis. A total of 12% (n = 62) survived to discharge. Patients with OHCAs in urban centres were 107% more likely to survive than those with OHCAs in rural areas (adjusted odds ratio = 2.1; 95% confidence interval = 1.1 to 3.8; P = 0.028). OHCAs in urban centres had a significantly shorter mean time to defibrillation of shockable rhythm (11.2 minutes ± 6.2) vs those in rural areas (17.5 minutes ± 17.3). Conclusions Nova Scotia has an urban vs rural disparity in OHCA care that is also seen in densely populated OHCA centres. Survival is improved in urban centres. Further improvements in overall survival, especially in rural areas, may arise from community engagement in OHCA recognition and optimized healthcare delivery.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".