Abstract 13608: Center Variation in Home Time Following Acute Ischemic Stroke
Bibliographic record
Abstract
Background: Stroke survivors have identified that home time is a high priority outcome, but there are limited data on factors associated with time at home following stroke as well as the degree to which home time varies among discharging hospitals. Methods: PROSPER is a PCORI-funded program designed with stroke survivors to evaluate post-stroke therapies and outcomes. We linked data for patients >65 years old who were enrolled in the GWTG-Stroke Registry to Medicare claims to ascertain home time, defined as time spent alive and out of a hospital or skilled nursing facility, at 90 days and 1 year after discharge for ischemic stroke. We used generalized mixed models with random effects to estimate adjusted mean home time for each hospital, accounting for patient characteristics. We then used linear regression to assess hospital factors associated with risk-adjusted home time. Results: From 2007-2011, 156,869 ischemic stroke patients at 1417 hospitals were linked to Medicare claims. Home time varied among hospitals, with overall unadjusted median home time (IQR) of 59.5 days (55.7-63.2) over the first 90 days and 270 days (256.0-281.1) over the first year. Hospital factors associated with more home time over 90 days included higher annual stroke volume; South, West, or Midwest geographic regions (vs. Northeast); and rural location (Table). Similar patterns were observed at 1 year. Compared with patients in the highest hospital home time quartile, patients in the lowest were older, more likely to be female, had more comorbidities, and had more severe strokes by NIHSS. Home time variation decreased after adjustment, with a median of 59.3 days (57.4-61.4) over 90 days and 270 days (266.3-274.2) over 1 year. Conclusions: In a population of older ischemic stroke survivors, home time after discharge varies by hospital stroke volume, severity case mix, and region. In adjusted analyses, hospital stroke volume and rural location were associated with more days at home following 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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".