Impact of Safety-Net Burden on In-Hospital Mortality and Hospitalization Costs Among Patients with Alcoholic Hepatitis and Alcoholic Cirrhosis
Why this work is in the frame
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Bibliographic record
Abstract
AIMS: Alcoholic hepatitis (AH) and alcoholic cirrhosis disproportionately affect ethnic minority and safety-net populations. We evaluate the impact of a hospital's safety net burden (SNB) on in-hospital mortality and costs among patients with AH and alcoholic cirrhosis. METHODS: We performed a cross-sectional analysis of 2012-2016 National Inpatient Sample. SNB was calculated as percentage of hospitalizations with Medicaid or uninsured payer status. Associations between hospital SNB and in-hospital mortality and costs were evaluated with adjusted multivariable logistic regression and linear regression models. RESULTS: Among 21,898 AH-related hospitalizations, compared to low SNB hospitals (LBH), patients hospitalized in high SNB hospitals (HBH) were younger (44.4 y vs. 47.4 y, P < 0.001) and more likely to be African American (11.3% vs. 7.7%, P < 0.001) or Hispanic (15.4% vs. 8.4%, P < 0.001). AH-related hospitalizations in HBH had a non-significant trend towards higher odds of mortality (OR 1.27, 95% CI 0.98-1.65, P = 0.07) and higher mean hospitalizations costs. Among 108,669 alcoholic cirrhosis-related hospitalizations, patients in HBH were younger (53.3 y vs. 55.8 y, P < 0.001) and more likely to be African American (8.2% vs. 7.3%, P < 0.001) or Hispanic (24.4% vs. 12.0%, P < 0.001) compared to LBH. Compared to alcoholic cirrhosis-related hospitalizations in LBH, mortality was higher among medium SNB (OR 1.10, 95% CI 1.03-1.17, P = 0.007) and HBH (OR 1.07, 95% CI 1.00-1.15, P = 0.05). Mean hospitalization costs were not different by SNB status. CONCLUSIONS: HBH hospitals predominantly serve ethnic minorities and underinsured/uninsured populations. The higher in-hospital mortality associated HBH particularly for alcoholic cirrhosis patients is alarming given its increasing burden in the USA.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 it