Effect of Ambient Workload in the Intensive Care Unit on Mortality and Time to Discharge Alive
Bibliographic record
Abstract
The purpose of this study was to determine the relationship between ambient workload and outcomes of patients in the intensive care unit (ICU). Measures of workload evaluated for each patient on each day of ICU admission were the number of new admissions, ICU census, "code blue" patients not admitted and Acute Physiology and Chronic Health Evaluation (APACHE) II scores and Multiple Organ Dysfunction Scores (MODSs) for admitted patients. Patients were defined as the patient at risk (the "index" patient) and the other patients in the ICU at the same time (the "non-index" patients). Logistic regression (for hospital mortality) and Cox proportional hazards regression (for time to discharge alive) were used to investigate the association between workload and outcomes. In total, 1,705 patients were included. Higher MODSs of non-index patients on the last day of the ICU admission were associated with lower mortality (odds ratio [OR] 0.82 per MODS point, 95% CI 0.72-0.94). A higher number of code blues during the ICU stay was associated with higher mortality (OR 1.18 per event, 95% CI 1.01-1.37). A higher ICU census and MODS of the non-index patients on the day of ICU admission were associated with a shorter time to discharge alive (hazard rate [HR] 1.03 per patient, 95% CI: 1.01-1.06, and 1.07 per MODS point, 95% CI:1.01-1.15, respectively).The association between measures of ambient workload in the ICU and patient outcomes is variable.Future resource planning and studies of patient safety would benefit from a prospective analysis of these factors to define workload limits and tolerances.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".