A global scientific strategy to cure hepatitis B
Bibliographic record
Abstract
Chronic hepatitis B virus (HBV) infection is a global public health challenge on the same scale as tuberculosis, HIV, and malaria. The International Coalition to Eliminate HBV (ICE-HBV) is a coalition of experts dedicated to accelerating the discovery of a cure for chronic hepatitis B. Following extensive consultation with more than 50 scientists from across the globe, as well as key stakeholders including people affected by HBV, we have identified gaps in our current knowledge and new strategies and tools that are required to achieve HBV cure. We believe that research must focus on the discovery of interventional strategies that will permanently reduce the number of productively infected cells or permanently silence the covalently closed circular DNA in those cells, and that will stimulate HBV-specific host immune responses which mimic spontaneous resolution of HBV infection. There is also a pressing need for the establishment of repositories of standardised HBV reagents and protocols that can be accessed by all HBV researchers throughout the world. The HBV cure research agenda outlined in this position paper will contribute markedly to the goal of eliminating HBV infection worldwide.
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 | 0.027 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.021 | 0.009 |
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".