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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| 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".