Optimal Timing of Transfer Out of the Intensive Care Unit
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it