New paradigms in hepatitis B management: only diamonds are forever
Bibliographic record
Abstract
INTRODUCTION: The hepatitis B virus (HBV) causes chronic hepatitis B (CHB) in ∼350 million people worldwide who have an increased risk of end-stage liver disease and/or hepatocellular carcinoma. SOURCES OF DATA: Several peer-reviewed papers featuring new approaches to anti-HBV management. Additionally, we also reviewed recent abstract presentations at international congresses. AREAS OF AGREEMENT: There has been great progress in CHB therapy with the development of standard and pegylated interferon (i.e. PEG-IFN) as well as nucleos/tide analogs (NAs). IFN has both antiviral and immunomodulatory effects and through immune-mediated destruction of infected hepatocytes offers the possibility of finite therapy. However, this 'killing for a cure' antiviral strategy may not be tolerated in many, especially in cirrhotic patients. NAs inhibit viral reverse transcriptase, have few side effects and prevent liver disease progression, but cannot offer a cure as they have little effect on the resilient HBV covalently closed circular DNA (cccDNA) intermediate. Moreover, NAs such as tenofovir and entecavir offer a high genetic barrier to resistance, but are expensive and not readily available in many global regions. GROWING POINTS: Despite significant treatment advances, there is increased recognition of the need for improved anti-HBV treatments, and new virologic tests for monitoring treatment response. AREAS OF CONTROVERSY: The role of quantitative hepatitis B surface antigen, intrahepatic cccDNA levels and viral genotype in selecting treatment candidates and refining NA stopping rules. AREAS TIMELY FOR DEVELOPING NEW RESEARCH: Potential new therapies include viral entry inhibitors, RNA interference technologies (i.e. RNAi) and small molecules that modulate cccDNA transcription, as well as novel immunomodulatory therapies to boost HBV-specific T cell responses. The ultimate goal of new tests and anti-HBV therapies is to reduce the burden and expense of life-long CHB treatment, as 'only diamonds are forever'.
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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 | 0.016 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.007 | 0.020 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.010 | 0.026 |
| Insufficient payload (model declined to judge) | 0.023 | 0.011 |
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".