Race/Ethnicity, Quality of Care, and Outcomes in Ischemic Stroke
Bibliographic record
Abstract
BACKGROUND: Prior studies suggest differences in stroke care associated with race/ethnicity. We sought to determine whether such differences existed in a population of black, Hispanic, and white patients hospitalized with stroke among hospitals participating in a quality-improvement program. METHODS AND RESULTS: We analyzed in-hospital mortality and 7 stroke performance measures among 397,257 patients admitted with ischemic stroke to 1181 hospitals participating in the Get With The Guidelines-Stroke program 2003 through 2008. Relative to white patients, black and Hispanic patients were younger and more often had diabetes mellitus and hypertension. After adjustment for both patient- and hospital-level variables, black patients had lower odds relative to white patients of receiving intravenous thrombolysis (odds ratio [OR], 0.84; 95% confidence interval [CI], 0.77 to 0.91), deep vein thrombosis prophylaxis (OR, 0.88; 95% CI, 0.83 to 0.92), smoking cessation (OR, 0.85; 95% CI, 0.79 to 0.91), discharge antithrombotics (OR, 0.88; 95% CI, 0.84 to 0.92), anticoagulants for atrial fibrillation (OR, 0.84; 95% CI, 0.75 to 0.94), and lipid therapy (OR, 0.91; 95% CI, 0.88 to 0.96), and of dying in-hospital (OR, 0.90; 95% CI, 0.85 to 0.95). Hispanic patients received similar care as their white counterparts on all 7 measures and had similar in-hospital mortality. Black (OR, 1.31; 95% CI, 1.28 to 1.35) and Hispanic (OR, 1.16; 95% CI, 1.11 to 1.20) patients had higher odds of exceeding the median length of hospital stay relative to whites. During the study, quality of care improved in all 3 race/ethnicity groups. CONCLUSIONS: Black patients with stroke received fewer evidence-based care processes than Hispanic or white patients. These differences could lead to increased risk of recurrent stroke. Quality of care improved substantially in the Get With The Guidelines-Stroke Program over time for all 3 racial/ethnic groups.
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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) | 0.000 | 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 teacher head, 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".