Association Between Consecutive Days Worked by Intensivists and Outcomes for Critically Ill Patients
Bibliographic record
Abstract
OBJECTIVE: To evaluate the association between consecutive days worked by intensivists and ICU patient outcomes. DESIGN: Retrospective cohort study linked with survey data. SETTING: Australia and New Zealand ICUs. PATIENTS: Adults (16+ yr old) admitted to ICU in the Australia New Zealand Intensive Care Society Centre for Outcome and Resource Evaluation Registries (July 1, 2016, to June 30, 2018). INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We linked data on staffing schedules for each unit from the Critical Care Resources Registry 2016-2017 annual survey with patient-level data from the Adult Patient Database. The a priori chosen primary outcome was ICU length of stay. Secondary outcomes included hospital length of stay, ICU readmissions, and mortality (ICU and hospital). We used multilevel multivariable regression modeling to assess the association between days of consecutive intensivist service and patient outcomes; the predicted probability of death was included as a covariate and individual ICU as a random effect. The cohort included 225,034 patients in 109 ICUs. Intensivists were scheduled for seven or more consecutive days in 43 (39.4%) ICUs; 27 (24.7%) scheduled intensivists for 5 days, 22 (20.1%) for 4 days, seven (6.4%) for 3 days, four (3.7%) for 2 days, and six (5.5%) for less than or equal to 1 day. Compared with care by intensivists working 7+ consecutive days (adjusted ICU length of stay = 2.85 d), care by an intensivist working 3 or fewer consecutive days was associated with shorter ICU length of stay (3 consecutive days: 0.46 d fewer, p = 0.010; 2 consecutive days: 0.77 d fewer, p < 0.001; ≤ 1 consecutive days: 0.68 d fewer, p < 0.001). Shorter schedules of consecutive intensivist days worked were also associated with trends toward shorter hospital length of stay without increases in ICU readmissions or hospital mortality. CONCLUSIONS: Care by intensivists working fewer consecutive days is associated with reduced ICU length of stay without negatively impacting mortality.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".