Temporal Differences in Out-of-Hospital Cardiac Arrest Incidence and Survival
Bibliographic record
Abstract
BACKGROUND: Understanding temporal differences in the incidence and outcomes of out-of-hospital cardiac arrest (OHCA) has important implications for developing preventative strategies and optimizing systems for OHCA care. METHODS AND RESULTS: We studied 18 588 OHCAs of presumed cardiac origin in patients aged ≥18 years who received resuscitative efforts by emergency medical services (EMS) and were enrolled in the Cardiac Arrest Registry to Enhance Survival (CARES) from October 1, 2005, to December 31, 2010. We evaluated temporal variability in OHCA incidence and survival to hospital discharge. There was significant variability in the frequency of OHCA by hour of the day (P<0.001), day of the week (P<0.001), and month of the year (P<0.001), with the highest incidence occurring during the daytime, from Friday to Monday, in December. Survival to hospital discharge was lowest for OHCA that occurred overnight (from 11:01 pm to 7 am; 7.1%) versus daytime (7:01 am to 3 pm; 10.8%) or evening (3:01 pm to 11 pm; 11.3%; P<0.001) and during the winter (8.8%) versus spring (11.1%), summer (11.0%), or fall (10.0%; P<0.001). There was no difference in survival to hospital discharge between OHCAs that occurred on weekends and weekdays (9.5% versus 10.4%, P=0.06). After multivariable adjustment for age, sex, race, witness status, layperson resuscitation, first monitored cardiac rhythm, and emergency medical services response time, compared with daytime and spring, survival to hospital discharge remained lowest for OHCA that occurred overnight (odds ratio, 0.81; 95% confidence interval, 0.70-0.95; P=0.008) and during the winter (odds ratio, 0.81; 95% confidence interval, 0.70-0.94; P=0.006), respectively. CONCLUSIONS: There is significant temporal variability in the incidence of and survival after OHCA. The relative contribution of patient pathophysiology, likelihood of the OHCA being observed, and prehospital and hospital-based resuscitative factors deserves further exploration.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".