Association Between Hospital Teaching Status and Outcomes After Out-of-Hospital Cardiac Arrest
Bibliographic record
Abstract
Background: Controversy exists about how best to organize systems of care for patients with out-of-hospital cardiac arrest (OHCA), as little evidence exists to guide policy-makers. In Canada, teaching hospitals are mainly cardiac referral centers that are potentially well suited towards treating patients with OHCA. Our objective was to determine whether patients with OHCA are more likely to survive if they present to teaching hospitals. Methods and Results: We conducted a retrospective observational cohort study by linking several population-based administrative databases in Ontario, Canada. All patients >20 years old who arrived alive to hospital after OHCA between April 1, 2007, and March 31, 2014, were eligible for inclusion. Patients with ST-segment–elevation myocardial infarction were excluded. The primary outcome was survival at 30 days. To determine the association between teaching status and 30-day survival, logistic regression models were used to adjust for baseline differences in patient characteristics. Prespecified analysis was performed stratified by age: ≤65, 66 to 80, and >80 years old. A total of 25 346 patients were included: 5413 at teaching and 19 933 at nonteaching hospitals. Survival at 30 days was 13.9% in teaching and 11.0% ( P <0.001) in nonteaching hospitals. Hospital teaching status was associated with a significantly higher adjusted odds of 30-day survival (odds ratio, 1.38 [95% CI, 1.14–1.67]). This improvement in survival was observed in younger patients (≤65 years: odds ratio, 1.41 [95% CI, 1.14–1.74]; 66 to 80 years: odds ratio,1.37 [95% CI, 1.13–1.67]), but there was no significant difference in the elderly (>80 years: odds ratio, 1.07 [95% CI, 0.79–1.44]). Conclusions: Patients with OHCA treated at teaching hospitals were more likely to survive to 30 days. These findings support current recommendations suggesting that treatment of these patients should be provided at specialized hospitals.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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".