Racial and Ethnic Differences in Outcomes in Older Patients With Acute Ischemic Stroke
Bibliographic record
Abstract
BACKGROUND: Little is known as to whether long-term outcomes of acute ischemic stroke (AIS) vary by race/ethnicity. Using the American Heart Association Get With The Guidelines-Stroke registry linked with Medicare claims data set, we examined whether 30-day and 1-year outcomes differed by race/ethnicity among older patients with AIS. METHODS AND RESULTS: We analyzed 200 900 patients with AIS >65 years of age (170 694 non-Hispanic whites, 85.0%; 20 514 non-Hispanic blacks, 10.2%; 6632 Hispanics, 3.3%; 3060 non-Hispanic Asian Americans, 1.5%) from 926 US centers participating in the Get With The Guidelines-Stroke program from April 2003 through December 2008. Compared with whites, other racial and ethnic groups were on average younger and had a higher median score on the National Institutes of Health Stroke Scale. Whites had higher 30-day unadjusted mortality than other groups (white versus black versus Hispanic versus Asian=15.0% versus 9.9% versus 11.9% versus 11.1%, respectively). Whites also had higher 1-year unadjusted mortality (31.7% versus 28.6% versus 28.1% versus 23.9%, respectively) but lower 1-year unadjusted all-cause rehospitalization (54.7% versus 62.5% versus 60.0% versus 48.6%, respectively). After risk adjustment, Asian American patients with AIS had lower 30-day and 1-year mortality than white, black, and Hispanic patients. Relative to whites, black and Hispanic patients had higher adjusted 1-year all-cause rehospitalization (black: adjusted odds ratio, 1.28 [95% confidence interval, 1.21-1.37]; Hispanic: adjusted odds ratio, 1.22 [95% confidence interval, 1.11-1.35]), whereas Asian patients had lower odds (adjusted odds ratio, 0.83 [95% confidence interval, 0.74-0.94]). CONCLUSIONS: Among older Medicare beneficiaries with AIS, there were significant differences in long-term outcomes by race/ethnicity, even after adjustment for stroke severity, other prognostic variables, and hospital characteristics.
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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 | 0.003 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".