Changing trends in aetiology‐based hospitalizations with end‐stage liver disease in the United States from 2016 to 2019
Bibliographic record
Abstract
BACKGROUNDS AND AIMS: A potent and safe antiviral agent may impact chronic hepatitis C (HCV)-related end-stage liver disease (ESLD). We assess aetiology-based hospitalizations for ESLD in the United States, 2016-2019. METHODS: We utilized the National Inpatient Sample (NIS) from 2016 to 2019. We defined ESLD as either decompensated cirrhosis or hepatocellular carcinoma, criteria obtained from the International Classification of Diseases, Tenth Revision. RESULTS: National hospitalization rates for non-alcoholic fatty liver disease (NAFLD) increased significantly from 67.1/100 000 persons in 2016 to 93.6 in 2019 with an average annual percentage change (AAPC) of 12.1%, while chronic hepatitis C (HCV) decreased significantly from 71.2/100 000 persons in 2016 to 58.5 in 2019 (-6.5% AAPC). Hospitalizations for ESLD in alcohol-related liver disease (ALD) increased as well. CONCLUSIONS: Hospitalization rates for NAFLD- and ALD-related ESLD increased steadily, while those for HCV-related ESLD decreased during the direct-acting antivirals era.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".