Treatment in disproportionately minority hospitals is associated with an increased mortality in end-stage liver disease
Bibliographic record
Abstract
BACKGROUND: Racial and ethnic disparities are a barrier in delivery of healthcare across the USA. Care for minority patients tends to be clustered into a small number of providers at minority hospitals, which has been associated with worse clinical outcomes in several conditions. However, the outcomes of treatment in patients with end-stage liver disease (ESLD) at predominately minority hospitals are unknown. We investigated the burden of the problem. METHODS: We utilized the nationwide in-patient sample (NIS) to conduct a retrospective nationwide cohort analysis. All patients >18 years of age admitted with ESLD were included in the analysis. A multivariate logistic regression model was used to study the mortality rate among patients with ESLD treated at minority hospitals compared to nonminority hospitals. RESULTS: A total of 53 281 467 hospitalizations from the 2008 to 2014 NIS were analyzed. There were 163 470 patients with ESLD that met inclusion criteria. In-hospital mortality rates for all races were 8.0 and 8.1% in black and Hispanic minority hospitals, respectively, compared to 7.3% in nonminority hospitals (P < 0.01). On multivariate analysis, treatment of ESLD in black and Hispanic minority hospitals was associated with 11% [odds ratio (OR), 1.11; 95% confidence interval (CI), 1.03-1.20; P < 0.01] and 22% (OR, 1.22; 95% CI, 1.09-1.37; P < 0.01) increased odds of death, respectively, compared to treatment in nonminority hospitals regardless of patient's race. CONCLUSION: Patients with ESLD treated at minority hospitals are faced with an increased mortality rate regardless of patient's race. This study highlights another quality gap that needs improvement to affect overall survival among patients with ESLD.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".