Efficacy of antiviral therapy in treating hepatitis B virus infections
Bibliographic record
Abstract
Roughly 292 million people worldwide carry chronic hepatitis B virus (HBV) infection, and less than 5% of those diagnosed currently receive antiviral treatment. This research evaluated the comparative efficacy of four antiviral regimens in achieving virological suppression, hepatitis B e-antigen (HBeAg) seroconversion, and liver function normalization among chronic HBV patients treated at a single Canadian center. A retrospective cohort of 244 treatment-naive chronic HBV patients was analyzed from records at Toronto Institute of Applied Sciences between March 2015 and February 2019. Patients received one of four regimens: tenofovir disoproxil fumarate (TDF, n = 87), entecavir (ETV, n = 74), tenofovir alafenamide (TAF, n = 52), or pegylated interferon alpha-2a (Peg-IFN, n = 31). The primary endpoint was virological suppression defined as HBV DNA levels below 20 IU/mL at week 96. Secondary endpoints included HBeAg seroconversion, alanine aminotransferase (ALT) normalization, and hepatitis B surface antigen (HBsAg) decline. At week 96, virological suppression rates were 89.2% for TDF, 85.1% for ETV, 92.4% for TAF, and 43.8% for Peg-IFN. The nucleos(t)ide analogue (NUC) groups all achieved significantly higher suppression rates compared to Peg-IFN (p10 mL/min) occurred in 8.1% of TDF patients versus 1.9% of TAF and 2.7% of ETV patients. Bone mineral density reductions were noted in 11.5% of TDF recipients. Peg-IFN was associated with more frequent adverse events including neutropenia (19.4%), fatigue (54.8%), and flu-like symptoms (67.7%). NUC-based regimens, particularly TAF, appear to offer the best balance of antiviral potency and safety for long-term management of chronic HBV. Peg-IFN retains a role in selected patients where finite therapy duration and immunological endpoints are prioritized over sustained virological suppression.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".