Clinical Predictors for Unsafe Direct Discharge Home Patients From Intensive Care Units
Bibliographic record
Abstract
PURPOSE: To describe factors (demographics and clinical characteristics) that predict patients who are at an increased risk of adverse events or unplanned return visits to a health-care facility following discharge direct to home (DDH) from intensive care units (ICUs). METHODS: Prospective cohort study of all adult patients who survived their stay in our medical-surgical-trauma ICU between February 2016 and 2017 and were discharged directly home. Patients were followed for 8 weeks postdischarge. Univariable and multivariable logistic regression analyses were performed to identify factors associated with adverse events or unplanned return visits to a health-care facility following DDH from ICU. RESULTS: < .2) were identified in the univariable analysis: prior substance abuse (odds ratio [OR] of URV of 2.50 [95% confidence interval: 1.08-5.80], hepatitis (OR: 6.92 [1.68-28.48]), sepsis (OR: 11.03 [1.19-102.29]), admission nine equivalents of nursing manpower score (NEMS) <24 (OR: 2.28 [1.03-5.04], no fixed address (OR: 22.9 [1.2-437.3]), ICU length of stay (LOS) <2 days (OR: 2.95 [1.28-6.78]), home discharge within London, Ontario (OR: 2.44 [1.00-5.92]), and left against medical advice (AMA; OR: 6.06 [2.04-17.98]). CONCLUSIONS: Our study identified 8 covariates that were potential predictors of URV: prior substance abuse, hepatitis, sepsis, admission NEMS <24, no fixed address, ICU LOS <2 days, home discharge within London, Ontario, and left AMA. The practice of direct discharges home from the ICU would benefit from adequately powered multicenter study in order to construct a clinical prediction model (that would require further testing and validation).
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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".