Hepatitis C as a Prognostic Indicator among Noncirrhotic Patients Hospitalized with Alcoholic Hepatitis
Bibliographic record
Abstract
OBJECTIVE: A nationwide analysis of alcoholic hepatitis (AH) admissions was conducted to determine the impact of hepatitis C virus (HCV) infection on short-term survival and hospital resource utilization. METHODS: Using the Nationwide Inpatient Sample, noncirrhotic patients admitted with AH throughout the United States between 1998 and 2006 were identified with diagnostic codes from the International Classification of Diseases, Ninth Revision. The in-hospital mortality rate (primary end point) of AH patients with and without co-existent HCV infection was determined. Hospital resource utilization was assessed as a secondary end point through linear regression analysis. RESULTS: From 1998 to 2006, there were 112,351 admissions for AH. In-hospital mortality was higher among patients with coexistent HCV infection (41.1% versus 3.2%; P=0.07). The adjusted odds of in-hospital mortality in the presence of HCV was 1.48 (95% CI 1.10 to 1.98). Noncirrhotic patients with AH and HCV also had longer length of stay (5.8 days versus 5.3 days; P<0.007) as well as greater hospital charges (US$25,990 versus US$21,030; P=0.0002). CONCLUSIONS: Among noncirrhotic patients admitted with AH, HCV infection was associated with higher in-hospital mortality and resource utilization.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".