Continuity of Care in Intensive Care Units: A Cluster-Randomized Trial of Intensivist Staffing
Bibliographic record
Abstract
RATIONALE: Little is known about the consequences of intensivists’ work schedules, or intensivist continuity of care. OBJECTIVES: To assess the impact of weekend respite for intensivists, with consequent reduction in continuity of care, on them and their patients. METHODS: In five medical intensive care units (ICUs) in four academic hospitals we performed a prospective, cluster-randomized, alternating trial of two intensivist staffing schedules. Daily coverage by a single intensivist in half-month rotations (continuous schedule) was compared with weekday coverage by a single intensivist, with weekend cross-coverage by colleagues (interrupted schedule). We studied consecutive patients admitted to study units, and the intensivists working in four of the participating units. MEASUREMENTS AND MAIN RESULTS: The primary patient outcome was ICU length of stay (LOS);we also assessed hospital LOS and mortality rates. The primary intensivist outcome was physician burnout. Analysis was by multivariable regression. A total of 45 intensivists and 1,900 patients participated in the study. Continuity of care differed between schedules (patients with multiple intensivists = 28% under continuous schedule vs. 62% under interrupted scheduling; P < 0.0001). LOS and mortality were nonsignificantly higher under continuous scheduling (ΔICU LOS 0.36 d, P = 0.20; Δhospital LOS 0.34 d, P = 0.71; ICU mortality, odds ratio = 1.43, P = 0.12; hospital mortality, odds ratio = 1.17,P = 0.41). Intensivists experienced significantly higher burnout, work–home life imbalance, and job distress working under the continuous schedule. CONCLUSIONS: Work schedules where intensivists received weekend breaks were better for the physicians and, despite lower continuity of intensivist care, did not worsen outcomes for medical ICU patients.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".