Trends of Readmissions for Gastrointestinal Bleeding After Alcoholic Hepatitis: Analysis of the Nationwide Readmission Database
Bibliographic record
Abstract
Background: Alcohol consumption is associated with numerous hepatic manifestations, including alcoholic fatty liver disease, alcoholic hepatitis (AH), and liver cirrhosis. AH is a common and serious complication of alcohol use. Gastrointestinal bleeding (GIB) remains one of the most common causes of death in these patients. In this article, we studied the trends of GIB after AH. Methods: This was a retrospective interrupted trend study. We analyzed the 2010, 2012, 2014, 2016, and 2018 Nationwide Readmission Databases. The first AH hospitalization in the year was marked as index hospitalization. We identified subsequent hospitalizations with GIB within 30 days and marked them as readmissions. A multivariate regression analysis was used to calculate the risk-adjusted odds of trends for GIB readmissions, including esophageal varices bleeding (EVB), upper GIB, lower GIB, and all GIB. Results: The volume of index hospitalizations increased from 10,248 in 2010 to 16,479 in 2018. Similarly, all readmissions increased from 1,838 in 2010 to 3,908 in 2018. Of all readmissions, EVB increased from 3.9% in 2010 to 5.9% in 2018 (odds ratio (OR) trend 1.10; P < 0.001). Readmissions for upper GIB increased from 2.4% in 2010 to 7.8% in 2018 (OR trend 1.22; P < 0.001). On the other hand, lower GIB readmissions decreased from 7.2% in 2010 to 4.7% in 2018 (OR trend 0.95; P = 0.015). There was no statistically significant trend for all GIB readmissions (OR trend 1; P = 0.915). Conclusion: Further studies are needed to evaluate the patterns of lower GIB in patients with liver disease and the recent trends of corticosteroids use in AH patients.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".