Contemporary Trends and Predictors of Postacute Service Use and Routine Discharge Home After Stroke
Bibliographic record
Abstract
BACKGROUND: Returning home after the hospital is a primary aim for healthcare; however, additional postacute care (PAC) services are sometimes necessary for returning stroke patients to their pre-event status. Recent trends in hospital discharge disposition specifying PAC use have not been examined across age groups or health insurance types. METHODS AND RESULTS: We examined trends in discharge to inpatient rehabilitation facilities (IRFs), skilled nursing facilities (SNFs), home with home health (HH), and home without services for 849 780 patients ≥18 years of age with ischemic or hemorrhagic stroke at 1687 hospitals participating in Get With The Guidelines-Stroke. Multivariable analysis was used to identify factors associated with discharge to any PAC (IRF, SNF, or HH) versus discharge home without services. From 2003 to 2011, there was a 2.1% increase (unadjusted P=0.001) in PAC use after a stroke hospitalization. Change was greatest in SNF use, an 8.3% decrease over the period. IRF and HH increased 6.9% and 3.6%, respectively. The 2 strongest clinical predictors of PAC use after acute care were patients not ambulating on the second day of their hospital stay (ambulation odds ratio [OR], 3.03; 95% confidence interval [CI], 2.86 to 3.23) and those who failed a dysphagia screen or had an order restricting oral intake (OR, 2.48; 95% CI, 2.37 to 2.59). CONCLUSIONS: Four in 10 stroke patients are discharged home without services. Although little has changed overall in PAC use since 2003, further research is needed to explain the shift in service use by type and its effect on outcomes.
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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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".