One‐year survival after out‐of‐ hospital cardiac arrest: Sex‐based survival analysis in a Canadian population
Bibliographic record
Abstract
Objective We investigated sex differences in 1-year survival in a cohort of patients who survived out-of-hospital cardiac arrest (OHCA) to hospital discharge. We hypothesized that female sex is associated with higher 1-year posthospital discharge survival. Methods A retrospective analysis of linked data (2011–2017) from clinical databases in British Columbia (BC) was conducted. We used Kaplan–Meier curves, stratified by sex, to display survival up to 1-year, and the log-rank test to test for significant sex differences. This was followed by multivariable Cox proportional hazards analysis to investigate the association between sex and 1-year mortality. The multivariable analysis adjusted for variables known to be associated with survival, including variables related to OHCA characteristics, comorbidities, medical diagnoses, and in-hospital interventions. Results We included 1278 hospital-discharge survivors; 284 (22.2%) were female. Females had a lower proportion of OHCA occurring in public locations (25.7% vs. 44.0%, P < 0.001), a lower proportion with a shockable rhythm (57.7% vs. 77.4%, P < 0.001), and fewer hospital-based acute coronary diagnoses and interventions. One-year survival for females and males was 90.5% and 92.4%, respectively (log-rank P = 0.31). Unadjusted (hazard ratio [HR] males vs. females 0.80, 95% confidence interval [CI] 0.51–1.24, P = 0.31) and adjusted (HR males vs. females 1.14, 95% CI 0.72–1.81, P = 0.57) models did not detect differences in 1-year survival by sex. Conclusion Females have relatively unfavorable prehospital characteristics in OHCA and fewer hospital-based acute coronary diagnoses and interventions. However, among survivors to hospital discharge, we found no significant difference between males and females in 1-year survival, even after adjustment.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".