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 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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".