Improving Temporal Trends in Survival and Neurological Outcomes After Out-of-Hospital Cardiac Arrest
Bibliographic record
Abstract
Background Considerable effort has gone into improving outcomes from out-of-hospital cardiac arrest (OHCA). Studies suggest that survival is improving; however, prior studies had insufficient data to pursue the relationship between markers of guideline compliance and temporal trends. The objective of the study was to evaluate trends in OHCA survival over an 8-year period that included the implementation of the 2005 and 2010 international cardiopulmonary resuscitation (CPR) guidelines. Methods and Results This was a population-based cohort study of all consecutive treated OHCA patients of presumed cardiac cause between 2006 and 2013 in the City of Toronto, Canada, and surrounding regions. Temporal changes were measured by χ 2 trend test. The association between year of the OHCA and survival was evaluated using logistic regression and joinpoint analysis. A total of 23 619 patients with OHCA met study inclusion criteria. During the study period, survival to hospital discharge doubled (4.8% in 2006 to 9.4% in 2013; P <0.0001), and survival with good neurological outcome increased (6.2% in 2010 to 8.5% in 2013; P =0.005). Improvements occurred in the rates of bystander CPR and automated external defibrillator application, high-quality CPR metrics, and in-hospital targeted temperature management. After adjusting for the Utstein variables, survival to hospital discharge (odds ratio, 1.12; 95% confidence interval, 1.09–1.15) and survival with good neurological outcome (odds ratio, 1.13; 95% confidence interval, 1.05–1.22) increased with each year of study. Conclusions Survival after OHCA has improved over time. This trend was associated with improved rates of bystander CPR, automated external defibrillator use, high-quality CPR metrics, and in-hospital targeted temperature management. The results suggest that multiple factors, each improving over time, may have contributed to the observed increase in survival.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".