HBsAg Loss as a Treatment Endpoint for Chronic HBV Infection: HBV Cure
Bibliographic record
Abstract
Despite the availability of effective vaccines and antiviral therapy over the past two to three decades, chronic hepatitis B virus (HBV) infection remains a major global health threat as a leading cause of cirrhosis and liver cancer. Functional HBV cure defined as hepatitis B surface antigen (HBsAg) loss and undetectable serum HBV DNA is associated with improved clinical outcomes in patients with chronic HBV infection. However, spontaneous loss of HBsAg is rare and occurs in only 1% of all HBsAg-positive individuals annually. Furthermore, the rate of functional cure with currently available antiviral therapy is even lower, <1% patients on treatment per year. Nonetheless, HBsAg loss has become the new target or therapeutic endpoint for antiviral treatment. Recently, there has been much excitement surrounding the development of novel antiviral agents such as small interfering RNA (siRNA), core assembly modulators (CAMs), nucleic acid polymers (NAPs) among others, which may be used in combination with nucleos(t)ide analogs and possibly immunomodulatory therapies to achieve functional cure in a significant proportion of patients with chronic hepatitis B. Novel assays with improved sensitivity for detection of very low levels of HBsAg and to determine the source of HBsAg production will also be required to measure efficacy of newer antiviral treatments for HBV cure. In this narrative review, we will define HBV cure, discuss various sources of HBsAg production, evaluate rates of HBsAg loss with current and future antiviral agents, review clinical factors associated with spontaneous HBsAg loss, and explore clinical implications of functional cure.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.002 | 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".