Impact of intensive care unit discharge time on patient outcome
Bibliographic record
Abstract
OBJECTIVE: To determine the impact of nighttime intensive care unit (ICU) discharge on patient outcome. DESIGN: Multiple-center, retrospective observational cohort study. SETTING: Canadian hospitals. PATIENTS: We used a prospectively collected dataset containing information on 79,090 consecutive admissions from 31 Canadian community and teaching hospitals. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Patients were categorized according to the time of ICU discharge into daytime (07:00-20:59) and nighttime (21:00-06:59). Admissions were excluded if the patients were a) </=16 yrs of age (392); b) admitted following cardiac surgery (6,641); c) admitted following the initial admission for patients readmitted to the ICU within the same hospital stay (3,632); d) admitted due to a lack of available ward or specialty care beds (457); or c) transferred to another acute care facility (7,724). We found that 62,056 patients were discharged to the ward following the initial ICU admission. Of the 47,062 discharges eligible for analyses, 10.1% were discharged at night. The unadjusted odds of death for patients discharged from ICU at night was 1.35 (95% confidence interval, 1.23, 1.49), compared with patients discharged during the daytime. After adjustment for illness severity, source, case-mix, age, gender, and hospital size, the mortality risk was increased by 1.22-fold (95% confidence interval, 1.10, 1.36) for nighttime discharges. Multivariate regression analysis revealed that patients discharged from the ICU at night have a significantly shorter ICU length of stay than those discharged during the day (p < .001). Whereas hospital length of stay was similar for daytime and nighttime discharges who survived hospital stay, patients discharged at night who did not survive hospital stay had a significantly shorter hospital length of stay (p = .002). CONCLUSIONS: Patients discharged from the ICU at night have an increased risk of mortality compared with those discharged during the day.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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 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".