A3 TREATMENT IN DISPROPORTIONATELY MINORITY HOSPITALS IS ASSOCIATED WITH AN INCREASED MORTALITY IN END STAGE LIVER DISEASE
Bibliographic record
Abstract
Abstract Background Racial and ethnic disparities continue to remain a barrier in delivery of health care across the United States. 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 end stage liver disease (ESLD) patients at predominately minority hospitals is unknown. Aims To evaluate in-hospital mortality rate among ESLD patients treated in minority hospitals compared to non-minority hospitals. Methods We utilized the Nationwide Inpatient 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 ESLD patients treated at minority hospitals compared to ESLD patients treated at non-minority hospitals. Results A total of 53,281,467 hospitalizations from the 2008–2014 NIS sample were analyzed. There were 163,470 patients with ESLD that met inclusion criteria. There were 10,178 (6.2%) and 31,226 (19.1%) ESLD patients treated at Black and Hispanic minority hospitals, respectively. In hospital mortality rate for all races were 8.0% and 8.1% in Black and Hispanic minority hospitals, respectively, compared to 7.3% in non-minority hospitals (p<0.01). On multivariate analysis, treatment of ESLD in Black and Hispanic minority hospitals were associated with a 11% (OR: 1.11; 95% 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 a non-minority hospital regardless of patient race. Conclusions ESLD patient treated at minority hospital are faced with an increased mortality rate regardless of a patients race. The current study highlights a quality gap that needs improvement to effect overall survival among ESLD patients. Funding Agencies None
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.006 | 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".