Long-term Safety of Directly Discharging Patients Home from the ICU Compared to Ward Transfer
Bibliographic record
Abstract
Purpose: To evaluate the long-term safety of directly discharging intensive care unit (ICU) survivors to their home. Methods: A retrospective observational cohort of 341 ICU survivors who were directly discharged home from the ICU (“direct discharge”) or discharged home ≤72 hours after ICU transfer to the ward (“ward transfer”) was conducted in Regina, Saskatchewan ICUs between September 1, 2016 and September 30, 2018. The primary outcome was 90-day hospital readmission. Secondary outcomes included 30-day, 90-day, and 365-day emergency department (ED) visits, 30-day and 365-day hospital readmissions, and 365-day mortality. All outcomes were evaluated by multivariable Cox regression after adjustment for demographic and clinical characteristics. Results: Of 341 survivors (25.5% of total ICU visits), 148 (43.4%) patients were direct discharges and 193 (56.6%) were ward transfers. The median age was 46 years (interquartile range, 34-62), 38.4% were female, and 61.8% resided in Regina. Compared to the ward transfer cohort, more patients in the direct discharge cohort had at least one 90-day hospital readmission (30.4% versus 17.1% of patients, adjusted hazard ratio 2.09, 95% confidence interval 1.28-3.40, P = .003), after adjustment. Additionally, there were more 90-day ED visits ( P = .045), and 30-day ( P = .049) and 365-day hospital readmissions ( P = .03), after adjustment. Conclusions: In Saskatchewan, direct discharge compared to ward transfer was associated with an increase in 90-day hospital readmissions, and potentially other clinical outcomes. Further study is necessary.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".