Aiming for Functional Cure With Established and Novel Therapies for Chronic Hepatitis B
Bibliographic record
Abstract
Chronic hepatitis B virus (HBV) infection remains difficult to cure due to the persistent, self-replenishing nature of the viral genome and impaired host immune responses. Current treatment goals for chronic hepatitis B (CHB) are to prevent or significantly delay liver-related adverse outcomes and death, and two types of treatments are available: nucleos(t)ide analogues (NAs) and interferons (IFNs). NAs effectively suppress HBV replication, and IFNs improve serological response rates, thereby decreasing the risk of adverse outcomes. However, their efficacy in attaining serological responses, especially functional cure (i.e., loss of serum hepatitis B surface antigen), is very limited. Various strategies such as stopping antiviral therapy or combining therapies have been investigated to enhance response, but efficacy is only modestly improved. Importantly, the development of novel direct-acting antivirals and immunomodulators is underway to improve treatment efficacy and enhance rates of functional cure. The present review provides an overview of the treatment goals and indications, the possibility of expanding indications, and the safety and efficacy of different treatment strategies involving established and/or novel therapies as we continue our search for a cure.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".