Racial/Ethnic Differences in Process of Care and Outcomes Among Patients Hospitalized With Intracerebral Hemorrhage
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Although racial/ethnic differences in care are pervasive in many areas of medicine, little is known whether intracerebral hemorrhage (ICH) care processes or outcomes differ by race/ethnicity. METHODS: We analyzed 123 623 patients with ICH (83 216 white, 22 147 black, 10 519 Hispanic, and 7741 Asian) hospitalized at 1199 Get With The Guidelines-Stroke hospitals between 2003 and 2012. Multivariable logistic regression with generalized estimating equation was used to evaluate the association among race, stroke performance measures, and in-hospital outcomes. RESULTS: Relative to white patients, black, Hispanic, and Asian patients were significantly younger, but more frequently had more severe stroke (median National Institutes of Health Stroke Scale, 9, 10, 10, and 11, respectively; P<0.001). After adjustment for both patient and hospital-level characteristics, black patients were more likely to receive deep venous thrombosis prophylaxis, rehabilitation assessment, dysphagia screening, and stroke education, but less likely to have door to computed tomographic time ≤25 minutes and smoking cessation counseling than whites. Both Hispanic and Asian patients had higher odds of dysphagia screening but lower odds of smoking cessation counseling. In-hospital all-cause mortality was lower for blacks (23.0%), Hispanics (22.8%), and Asians (25.3%) than for white patients (27.6%). After risk adjustment, all minority groups had lower odds of death, of receiving comfort measures only or of being discharged to hospice. In contrast, they were more likely to exceed the median length of stay when compared with white patients. CONCLUSIONS: Although individual quality indicators in ICH varied by race/ethnicity, black, Hispanic, and Asian patients with ICH had lower risk-adjusted in-hospital mortality than white patients with ICH.
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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 | 0.004 |
| 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 | 0.000 |
| 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".