Abstract 18919: Race/Ethnic Differences in Mortality Among Patients Hospitalized with Intracerebral Hemorrhage
Bibliographic record
Abstract
Background: Despite higher burden of stroke in minorities, limited data exists in comparing mortality for patients with intracerebral hemorrhage of different racial and ethnic background. Methods: Data from 123,736 patients (83,280 white, 22,165 black, 10,541 Hispanic, and 7,750 other race) with intracerebral hemorrhage admitted to 1,199 Get With The Guidelines-Stroke hospitals between 2003 and 2012. Multivariate logistic regressions with generalized estimating equations were performed to evaluate the association between race and in-hospital mortality. Results: Compared with white patients, black, other race, and Hispanic patients were younger (median 75, 59, 67, and 64), had less comorbidities except for diabetes mellitus and hypertension, and had more severe stroke (National Institutes of Health Stroke Scale [NIHSS], median 9, 10, 11, and 10; all p<0.001). Black (23.0%, adjusted OR 0.91, 95% CI 0.87-0.95), Hispanic (22.8%, adjusted OR 0.85, 95% CI 0.79-0.91), and other racial/ethnic patients (25.3%, adjusted OR 0.87, 95% CI 0.81-0.93) were less likely to die in hospital than white patients (27.6%) after adjustment for patient and hospital characteristics. The mortality differences remained consistent after further adjustment for NIHSS in NIHSS complete records (N=47,436). To determine whether race/ethnic differences in mortality varied by age, we examined the interaction between race and age (p <0.009). The survival advantage was observed in older age groups but was not evident in younger age groups (Table). In contrast to lower mortality, minorities had longer length of stay than white patients (median 6, 6, 6, and 5 days for black, Hispanic, other, and white respectively, p<0.001). Conclusion: Among patients hospitalized with intracerebral hemorrhage, black, Hispanic, and other race/ethnic groups have lower risk of in-hospital mortality compared to white patients, though these race/ethnic differences were confined to patients age 60 and older.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".