Severe Hepatic Steatosis Is Associated With Low-Level Viremia and Advanced Fibrosis in Patients With Chronic Hepatitis B in North America
Bibliographic record
Abstract
Background and Aims The obesity epidemic has increased the risk of nonalcoholic fatty liver disease (NAFLD) in both the general and chronic hepatitis B (CHB) populations. Our study aims to determine the prevalence of NAFLD in patients with CHB based on controlled attenuation parameter (CAP) and the epidemiological, clinical, and virological factors associated with severe hepatic steatosis. Methods The Canadian Hepatitis B Network cohort was utilized to provide a cross-sectional description of demographics, comorbidities, antiviral treatment, and hepatits B virus (HBV) tests. Liver fibrosis and steatosis were measured by transient elastography and CAP, respectively. Any grade and severe steatosis were defined as CAP >248 and >280 dB/m, respectively. Advanced liver fibrosis was defined as transient elastography measurement >10.7 kPa. Results In 1178 patients with CHB (median age: 47.4%, 57.7% males, 75.7% Asian, 13% African, 6.5% White, 86% HBV e antigen negative, median HBV DNA of 2.44 log 10 IU/mL, 42.7% receiving treatment), the prevalence of any grade and severe steatosis was 53% and 36%, respectively. In the multivariate analysis, obesity was a significant predictor for severe steatosis (adjusted odds ratio: 5.046, 95% confidence interval: 1.22–20.93). Severe steatosis was a determinant associated with viral load (adjusted odds ratio: 0.385, 95% confidence interval: 0.20–0.75, P < .01; r = −0.096, P = .007) regardless of antiviral therapy, age, and alanine aminotransferase levels. Conclusion In this large multiethnic CHB population, hepatic steatosis is common. Severe steatosis is independently associated with higher fibrosis, but negatively with HBV DNA, regardless of antiviral therapy history.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".