The Effect of Hospital Safety-Net Burden and Patient Ethnicity on In-Hospital Mortality Among Hospitalized Patients With Cirrhosis
Bibliographic record
Abstract
BACKGROUND: Over 2.1 million individuals in the United Stats have cirrhosis, including 513,000 with decompensated cirrhosis. Hospitals with high safety-net burden disproportionately serve ethnic minorities and have reported worse outcomes in surgical literature. No studies to date have evaluated whether hospital safety-net burden negatively affects hospitalization outcomes in cirrhosis. We aim to evaluate the impact of hospitals' safety-net burden and patients' ethnicity on in-hospital mortality among cirrhosis patients. METHODS: Using National Inpatient Sample data from 2012 to 2016, the largest United States all-payer inpatient health care claims database of hospital discharges, cirrhosis-related hospitalizations were stratified into tertiles of safety-net burden: high (HBH), medium (MBH), and low (LBH) burden hospitals. Safety-net burden was calculated as percentage of hospitalizations per hospital with Medicaid or uninsured payer status. Multivariable logistic regression evaluated factors associated with in-hospital mortality. RESULTS: Among 322,944 cirrhosis-related hospitalizations (63.7% white, 9.9% black, 15.6% Hispanic), higher odds of hospitalization in HBHs versus MBH/LBHs was observed in blacks (OR, 1.26; 95%CI, 1.17-1.35; P<0.001) and Hispanics (OR, 1.63; 95% CI, 1.50-1.78; P<0.001) versus whites. Cirrhosis-related hospitalizations in MBHs or HBHs were associated with greater odds of in-hospital mortality versus LBHs (HBH vs. LBH: OR, 1.05; 95% CI, 1.00-1.10; P=0.044). Greater odds of in-hospital mortality was observed in blacks (OR, 1.27; 95% CI, 1.21-1.34; P<0.001) versus whites. CONCLUSION: Cirrhosis patients hospitalized in HBH experienced 5% higher mortality than those in LBH, resulting in significantly greater deaths in cirrhosis patients. Even after adjusting for safety-net burden, blacks with cirrhosis had 27% higher in-hospital mortality compared with whites.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".