Nighttime Cross-Coverage is Associated with Decreased Intensive Care Unit Mortality. A Single-Center Study
Bibliographic record
Abstract
RATIONALE: Cross-coverage is associated with medical errors caused by miscommunication during handoffs. However, no direct evidence links handoffs to outcomes, or explains the mechanisms leading to outcomes. Furthermore, the previous literature may overestimate the impact of handoffs because of hindsight bias. OBJECTIVES: To explore the effects of nighttime cross-coverage on mortality and decision making in critically ill patients. METHODS: Observational cohort of 629 consecutive critically ill admissions, admitted for at least 48 hours, and critical care fellows in an academic hospital. MEASUREMENTS AND MAIN RESULTS: Intensive care unit (ICU) mortality and nighttime decisions. Our exposure variable was cross-covering status of fellows. We observed a decrease in ICU mortality (odds ratio, 0.77 per 1 d; 0.60-0.99; P = 0.04), a higher number of nighttime decisions (19.3 vs. 10.4%; odds ratio, 2.02; 95% confidence interval [CI], 1.03-3.95; P = 0.04), an increase in fentanyl equivalents administered to patients at night (difference, +10.2 μg/h; 95% CI, +1.4 to +19.0; P = 0.02), and an increase in transfusions at night (difference, +465 ml; 95% CI, +98 to +832; P = 0.01) when fellows were cross-covering. CONCLUSIONS: In this single-center study exposure to cross-covering fellows was associated with a decrease in ICU mortality and with more nighttime decisions. Our findings contradict the dominant hypothesis that cross-coverage is associated with worse outcomes, and suggest that a "second look" by cross-covering fellows may mitigate cognitive errors. Future interventions to improve patient safety in ICUs should focus both on the quality of handoffs and on strategies to decrease cognitive errors.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".