Hepatitis B Virus Genotype Influence on Virological and Enzymatic Measures over Time—A Retrospective Longitudinal Cohort Study
Bibliographic record
Abstract
HBV is a hepatotropic virus with multiple genotypes. It is uncertain if specific genotype(s) influence virological measures and/or liver markers over time. It is unclear whether nucleos(t)ide analogue therapy response is influenced by genotype. In this retrospective longitudinal study, we utilized data from The Ottawa Hospital Viral Hepatitis Program (TOHVHP) to evaluate the role of HBV genotype on viral load, liver enzymatic levels, fibrosis progression, and parenchymal inflammation and steatosis over time. HBV DNA, ALT, and AST levels, as well as transient elastography scores for fibrosis (E) and inflammation/steatosis (CAP), were modeled using mixed-effects linear regression. Interaction terms between HBV genotype and time were included to investigate if there was a difference in trends between genotypes. A total of 393 HBV patients infected with genotypes A-E were included. The mean age was 44.4 years, and 56% were male. Asian (50.5%), Black (29.1%), and White (6.4%) patients were well-represented. By multivariate analysis, we found no evidence that the trajectories of these commonly measured viral or liver measures varied over time by HBV genotype in those receiving HBV nucleos(t)ides and in those not on antiviral therapy.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".