Time for a globally unified chronic HBV terminology?
Bibliographic record
Abstract
The terminology used to describe chronic hepatitis B (CHB) infection remains inconsistent and fragmented across liver societies, clinical settings, and research domains. This lack of alignment poses barriers to care, complicates clinical trial design, and can generate confusion among providers, people living with hepatitis B, and researchers. This article examines the impact of discordant CHB infection terminology on care delivery and research, highlighting specific challenges with commonly used terms, such as "immune tolerant," "indeterminate" or "grey zone", as well as with terms used for hepatitis B surface antigen loss, including "resolved infection", "occult infection" or "functional cure." Although recent guidelines have moved towards simplification, global uniformity remains lacking, particularly regarding definitions of disease phases and thresholds for initiating treatment. We call for alignment of terminology to improve care, increase treatment uptake, enhance patient engagement, and accelerate HBV research and elimination efforts. We propose a multistakeholder consensus process to create a unified and practical nomenclature that distinguishes between terminology for clinical care and terminology for research and drug development. We also call for intentional inclusion of people with lived experience in this process to ensure the language used is meaningful, empowering, and stigma-free. With the HBV field on the cusp of transformative therapies and simplified treatment algorithms, now is the time to harmonise the language we use. A globally unified chronic HBV infection terminology stands to enhance access to care, improve comparability of research data, and strengthen collaboration across the HBV community - all of which are critical to accelerating progress towards hepatitis B elimination.
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 | 0.111 | 0.151 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.007 | 0.029 |
| Scholarly communication | 0.020 | 0.042 |
| Open science | 0.006 | 0.019 |
| Research integrity | 0.012 | 0.033 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".