Abstract 17: Racial and Ethnic Differences in Outcomes in Older Patients with Acute Ischemic Stroke
Bibliographic record
Abstract
Background: Past studies suggest racial and ethnic differences in quality of care and in-hospital outcomes for acute ischemic stroke. Using the American Heart Association Get With The Guidelines-Stroke (GWTG-Stroke) registry linked to Medicare claims data, we examined whether 30-day and 1-year outcomes differed by race or ethnicity in those aged 65+ with acute ischemic stroke. Methods: We analyzed 200,900 acute ischemic stroke patients age 65 years or older (170,694 non-Hispanic whites, 85.0%; 20,514 non-Hispanic blacks, 10.2%; 6632 Hispanics, 3.3%; 3060 Asian Americans, 1.5%) from 926 U.S. centers participating in the GWTG-stroke program between 2003 and 2008. Both 30-day and 1-year unadjusted and risk adjusted outcomes from admission were examined. Results: Compared with whites, other racial and ethnic groups were on average younger and had higher median score on NIH stroke scale (NIHSS). There were significant racial and ethnic differences in 30-day unadjusted mortality rate (white: 15.0%, black: 9.9%, Hispanic: 11.9%, Asian: 11.1%), 1-year unadjusted mortality rate (white: 31.7%, black: 28.6%, Hispanic: 28.1%, Asian: 23.9%), and 1-year unadjusted all-cause rehospitalization after discharge (white: 54.7%, black: 62.5%, Hispanic: 60.0%, Asian: 48.6%). A slightly increasing trend for 30-day and 1-year unadjusted mortality from admission was observed in the overall sample. White patients had the highest unadjusted and risk adjusted 30-day mortality. By 1 year, white, black, and Hispanic patients all had similar adjusted outcomes yet Asian patients had persistently better results. With regard to 1-year all-cause rehospitalization rates, relative to whites, blacks and Hispanics had higher adjusted 1-year all-cause and stroke associated rehospitalization rates whereas Asians had lower adjusted rates (see Table). Conclusion: White older ischemic stroke patients have the highest 30-day mortality rates of any race/ethnicity group. By one year, only Asian patients have differentially lower mortality risk. In contrast, black and Hispanic patients have higher readmission risk to one year. 1
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".