Hospital Variation in Home-Time After Acute Ischemic Stroke
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Stroke survivors identify home-time as a high-priority outcome; there are limited data on factors influencing home-time and home-time variability among discharging hospitals. METHODS: We ascertained home-time (ie, time alive out of a hospital, inpatient rehabilitation facility, or skilled nursing facility) at 90 days and 1-year post discharge by linking data from Get With The Guidelines-Stroke Registry patients (≥65 years) to Medicare claims. Using generalized linear mixed models, we estimated adjusted mean home-time for each hospital. Using linear regression, we examined associations between hospital characteristics and risk-adjusted home-time. RESULTS: We linked 156 887 patients with ischemic stroke at 989 hospitals to Medicare claims (2007-2011). Hospital mean home-time varied with an overall unadjusted median of 59.5 days over the first 90 days and 270.2 days over the first year. Hospital factors associated with more home-time over 90 days included higher annual stroke admission volume (number of ischemic stroke admissions per year); South, West, or Midwest geographic regions (versus Northeast); and rural location; 1-year patterns were similar. Lowest home-time quartile patients (versus highest) were more likely to be older, black, women, and have more comorbidities and severe strokes. Home-time variation decreased after risk adjustment (interquartile range, 57.4-61.4 days over 90 days; 266.3-274.2 days over 1 year). In adjusted analyses, increasing annual stroke volume and rural location were associated with significantly more home-time. CONCLUSIONS: In older ischemic stroke survivors, home-time post discharge varies by hospital annual stroke volume, severity of case-mix, and region. In adjusted analyses, annual ischemic stroke admission volume and rural location were associated with more home-time post stroke.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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; both teacher heads agree on what is shown here.
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".