High occupancy increases the risk of early death or readmission after transfer from intensive care*
Bibliographic record
Abstract
OBJECTIVE: To determine whether a lack of intensive care unit beds was leading to premature patient discharge from the intensive care unit and subsequent early readmission or death. DESIGN: Prospective cohort study. SETTING: A single Canadian tertiary care teaching hospital. PATIENTS: All intensive care unit admissions between January 1, 1989 and December 31, 1996 were collected prospectively for inclusion in a registry database. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: There was a positive correlation between early readmission or death and average quarterly intensive care unit percent occupancy (p = .001). During the study period, 8693 patients experienced 10,185 admissions to intensive care. Of the 8222 patients remaining under active treatment (patients under palliative care were excluded), there were 455 (5.5%) adverse events (431 intensive care unit readmissions and 24 deaths) in the first 7 days post intensive care unit discharge. Patients requiring a new surgical intervention with postoperative intensive care unit admission were not considered readmissions. In a multivariate analysis, significant risk factors for an adverse event included age >35 yrs, particular diagnoses (respiratory diagnoses, sepsis, neurosurgery, thoracic surgery, and gastrointestinal diagnoses), Acute Physiology and Chronic Health Evaluation II score, and intensive care unit length of stay. Discharge from the intensive care unit at a time of no vacancy was also a significant risk factor for intensive care unit readmission or unexpected death with an adjusted relative risk of 1.56 (95% confidence interval 1.05, 2.31). CONCLUSIONS: Increased patient occupancy within an intensive care unit is associated with an increased risk of early death or intensive care unit readmission post intensive care unit discharge. Overloading the capacity of an intensive care unit to care for critically ill patients may affect physician decision-making, resulting in premature discharge from the intensive care unit.
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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 | 0.003 |
| 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".