Abstract WP375: Quality of Care and In-Hospital Outcomes by Race and Ethnicity among Patients Hospitalized with Intracerebral Hemorrhage: Findings from 123,623 Patients in the Get With The Guidelines-Stroke Program
Bibliographic record
Abstract
Background: The rates of intracerebral hemorrhage (ICH) are disproportionally higher in minorities. While racial/ethnic differences in care persist in many areas of medicine, no study to date has examined whether ICH care processes or outcomes differ by patient race or ethnicity. Methods: We analyzed data from 123,623 ICH patients (83,216 white; 22,147 black; 10,519 Hispanic; and 7,741 Asian) hospitalized at 1,199 Get With The Guidelines-Stroke hospitals between 2003 and 2012. Multivariate logistic regression with generalized estimating equation was used to evaluate the association between race, stroke performance measures, and in-hospital outcomes. Results: Relative to white ICH patients, black, Hispanic, and Asian ICH patients were younger, more frequently had diabetes mellitus, hypertension, and more severe stroke (median National Institutes of Health Stroke Scale [NIHSS]:9, 10, 10, and 11, respectively, p<0.001). After adjusting for both patient- and hospital-level characteristics (Table), black ICH patients were more likely than whites to receive deep venous thrombosis prophylaxis, rehabilitation assessment, dysphagia screening, and stroke education, but less likely to receive smoking cessation counseling despite high prevalence of black current smokers. All minority groups had lower rates of in-hospital mortality (27.6%. 23.0%, 22.8%, and 25.3% for white, black, Hispanic, and Asian, respectively; p<0.001), but were more likely to experience a longer length of stay (median 5, 6, 6, and 6 days, respectively; p<0.001) than white patients. These differences remained consistent after further adjustment for NIHSS among NIHSS complete records (N=47,408). Conclusion: We found no clear pattern of racial or ethnic differences in the quality of care delivered to ICH patients. Black, Hispanic, and Asian ICH patients had lower risk-adjusted mortality compared with their white counterparts.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".