Bibliographic record
Abstract
The hepatitis B virus (HBV) is a global public health problem with more than 240 million people chronically infected worldwide, who are at risk for end-stage liver disease and hepatocellular carcinoma. There are an estimated 600000 deaths annually from complications of HBV-related liver disease. Antiviral therapy with nucleos/tide analogs (NA) targeting the HBV polymerase (P) can inhibit disease progression by long-term suppression of HBV replication. However, treatment may fail with first generation NA therapy due to the emergence of drug-resistant mutants, as well as incomplete medication adherence. The HBV replicates via an error-prone reverse transcriptase leading to quasispecies. Due to overlapping open reading frames mutations within the HBV P can cause concomitant changes in the HBV surface gene (S) and vice versa. HBV quasispecies diversity is associated with response to antiviral therapy, disease severity and long-term clinical outcomes. Specific mutants have been associated with antiviral drug resistance, immune escape, liver fibrosis development and tumorgenesis. An understanding of HBV variants and their clinical relevance may be important for monitoring chronic hepatitis B disease progression and treatment response. In this review, we will discuss HBV molecular virology, mechanism of variant development, and their potential clinical impact.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".