Optimal Timing of Transfer Out of the Intensive Care Unit
Bibliographic record
Abstract
BACKGROUND: Little other than subjective judgment is available to help clinicians determine when a patient should be transferred out of the intensive care unit. OBJECTIVE: To assess whether remaining in the intensive care unit longer than judged to be medically necessary is associated with increased 30-day mortality. METHODS: This prospective, observational cohort study was performed in a 13-bed, closed-model, adult medical intensive care unit of a county-owned, university-affiliated hospital that often has difficulty transferring patients to general care areas because of a lack of available beds. Analysis included all 2401 survivors of intensive care from the study period. Delay in discharge from the intensive care unit was defined as time elapsed between the request for transfer and the actual transfer. Logistic regression was used to assess the association of discharge delay with 30-day mortality, adjusting for demographics, comorbid conditions, type and severity of acute illness, care limitations in the unit, and other potential confounding variables. Nonlinear relationships with continuous variables were modeled with restricted cubic splines. RESULTS: Overall, 30-day mortality was 10.1%. Mean discharge delay was 9.6 (SD, 11.7) hours; 9.9% had a discharge delay exceeding 24 hours. The relationship of 30-day mortality to discharge delay was statistically significant and U-shaped, with the nadir at 20 hours. CONCLUSIONS: These data indicate an optimal time window for patients to leave the intensive care unit, with increased mortality not only if they leave earlier but also if they leave later than this optimal timing.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".