Abstract 13260: Trends in Survival From In-hospital Cardiac Arrests During Nights and Weekends
Bibliographic record
Abstract
Background: In-hospital cardiac arrest (IHCA) survival is worse during nights and weekends. Although survival and neurologic outcomes after IHCA have improved in recent years, it remains unknown whether the above trends are consistent in patients with IHCA during nights and weekends. Methods: We identified 151,071 adults at 470 U.S. hospitals in the Get with the Guidelines-Resuscitation registry during 2000-2014. Using multivariable logistic regression with generalized estimating equations, we examined temporal trends in survival to discharge and determined whether trends in IHCA differed during on-hours (Monday - Friday 7:00 am - 10:59 pm) vs. off-hours (Monday - Friday 11:00 pm - 6:59 am, Saturday-Sunday, all day). We also examined acute resuscitation and post-resuscitation survival as secondary outcomes. Results: Among 151,071 participants, 79,091 (52.4%) had an IHCA during off-hours. Risk-adjusted survival proportions improved over time in both groups ( on-hours : 16.0% in 2000, 25.2% in 2014; off-hours : 11.9% in 2000, 21.9% in 2014; P for trend < .001 for both). However, temporal trends in survival did not differ between on-hours and off-hours both on absolute (P=0.75) and relative scale (P = .059, Table). Acute resuscitation survival improved significantly in both groups ( on-hours : 56.1% in 2000, 71% in 2014; off-hours : 46.9% in 2000, 68.2% in 2014; P for trend < .001 for both) such that difference between on-hours and off-hours narrowed over time (P=0.02 absolute scale, P < .001 relative scale, Table). In contrast, although post-resuscitation survival improved over time in both groups (P for trend < .001), the absolute and relative differences persisted in both groups (Table). Conclusions: Despite a significant temporal reduction in acute resuscitation survival difference for IHCA during on-hours compared to off-hours, differences in overall survival persist between the groups, highlighting an important area for quality 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".