Chronic Hepatitis B Finite Treatment: Similar and Different Concerns With New Drug Classes
Bibliographic record
Abstract
Chronic hepatitis B, a major cause of liver disease and cancer, affects >250 million people worldwide. Currently there is no cure, only suppressive therapies. Efforts to develop finite curative hepatitis B virus (HBV) therapies are underway, consisting of combinations of multiple novel agents with or without nucleos(t)ide reverse-transcriptase inhibitors. The HBV Forum convened a webinar in July 2021, along with subsequent working group discussions to address how and when to stop finite therapy for demonstration of sustained off-treatment efficacy and safety responses. Participants included leading experts in academia, clinical practice, pharmaceutical companies, patient representatives, and regulatory agencies. This Viewpoints article outlines areas of consensus within our multistakeholder group for stopping finite therapies in chronic hepatitis B investigational studies, including trial design, patient selection, outcomes, biomarkers, predefined stopping criteria, predefined retreatment criteria, duration of investigational therapies, and follow-up after stopping therapy. Future research of unmet needs 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 | 0.058 | 0.090 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".