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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".