Management of Antiviral Resistance in Patients with Chronic Hepatitis B
Bibliographic record
Abstract
A meeting of physicians and scientists involved in the management of chronic hepatitis B (CHB) was held to review current scientific data regarding antiviral resistance in hepatitis B virus (HBV) infection. The goals of the meeting were to describe current treatments for CHB, discuss emerging issues in HBV drug resistance and to delineate patient monitoring, including markers for resistance, during administration of antiviral therapy. The aim of this review article is to provide treating physicians with a framework for the management of CHB in the context of antiviral resistance. Definitions of primary and secondary antiviral treatment failure can be used to aid monitoring and early diagnosis of drug resistance before disease progression occurs as a consequence of viral breakthrough. Primary antiviral treatment failure is defined as failure of a drug to reduce HBV DNA levels by > or = 1 x log10 IU/ml within 3 months following initiation of therapy, and secondary antiviral treatment failure as a rebound of HBV replication of > or = 1 x log10 IU/ml from nadir in patients with an initial antiviral treatment effect (> or = 1 x log10 IU/ml decrease in serum HBV DNA). Confirmation of antiviral drug failure can be established by sequencing the HBV DNA polymerase and identifying specific genetic markers of antiviral drug resistance. In addition to virological assays, HBV resistance can be assessed from a clinical perspective including increased serum alanine aminotransferase levels and the development of systemic symptoms or signs of liver failure. Potential strategies to prevent the emergence of resistance and how to manage drug-resistant HBV once it emerges are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".