Racial/ethnic differences in inpatient mortality and use of institutional postacute care following subarachnoid hemorrhage
Bibliographic record
Abstract
OBJECT: The goal of this study was to determine racial/ethnic differences in inpatient mortality rates and the use of institutional postacute care following subarachnoid hemorrhage (SAH) in the U.S. METHODS: A cross-sectional study of hospital discharges for SAH was conducted using the Nationwide Inpatient Sample for the years 2005-2010. Discharges with a principal diagnosis of SAH were identified and abstracted using the appropriate ICD-9-CM diagnostic code. Racial/ethnic groups were defined as white, black, Hispanic, Asian/Pacific Islander (API), and American Indian. Multinomial logistic regression analyses were performed comparing racial/ethnic groups with respect to the primary outcome of risk of in-hospital mortality and the secondary outcome of likelihood of discharge to institutional care. RESULTS: During the study period, 31,631 discharges were related to SAH. Race/ethnicity was a significant predictor of death (p = 0.003) and discharge to institutional care (p ≤ 0.001). In the adjusted analysis, compared with white patients, API patients were at higher risk of death (OR 1.34, 95% CI 1.13-1.59) and Hispanic patients were at lower risk of death (OR 0.84, 95% CI 0.72-0.97). The likelihood of discharge to institutional care was statistically similar between white, Hispanic, API, and Native American patients. Black patients were more likely to be discharged to institutional care compared with white patients (OR 1.27, 95% CI 1.14-1.40), but were similar to white patients in the risk of death. CONCLUSIONS: Significant racial/ethnic differences are present in the risk of inpatient mortality and discharge to institutional care among patients with SAH in the US. Outcome is likely to be poor among API patients and best among Hispanic patients compared with other 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".