Trends in Short- and Long-Term Survival Among Out-of-Hospital Cardiac Arrest Patients Alive at Hospital Arrival
Bibliographic record
Abstract
BACKGROUND: Out-of-hospital cardiac arrest (OHCA) is associated with a poor prognosis and poses a significant burden to the healthcare system, but few studies have evaluated whether OHCA incidence and survival have changed over time. METHODS AND RESULTS: A population-based cohort study was conducted, including 34 291 OHCA patients >20 years of age who were transported alive to the emergency department of an acute-care hospital from April 1, 2002, to March 31, 2012, in Ontario, Canada. Patients with life-threatening trauma and those who died before hospital arrival were excluded. The overall age- and sex-standardized incidence of OHCA patients who were transported alive was 36 cases per 100 000 persons and did not significantly change over the study period. Cardiac risk factor prevalence increased significantly, whereas the rate of most cardiovascular conditions decreased significantly. The 30-day survival improved from 9.4% in 2002 to 13.6% in 2011; 1-year survival improved from 7.7% to 11.8% (P<0.001). Patients hospitalized in 2011 were significantly more likely to survive 30 days (adjusted odds ratio, 1.47 [95% CI, 1.22-1.77]) and 1 year (adjusted odds ratio, 1.55 [95% CI, 1.27-1.91]) compared with 2002. A significant interaction between temporal trends in survival improvement and age group was observed in which the improvement in survival was largest in the youngest age groups. CONCLUSIONS: OHCA patients who were transported alive are increasingly likely to have cardiovascular risk factors but less likely to have previous cardiovascular conditions. The overall incidence of OHCA patients transported to hospital alive did not change over the past decade. Short- and longer-term survival after OHCA has substantially improved, with younger patients experiencing the greatest improvement.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".