J-103 The International Coalition to Eliminate Hepatitis B (ICE-HBV)
Bibliographic record
Abstract
Background: Over 257 million people worldwide are chronically infected with hepatitis B virus (HBV), resulting in over 880,000 deaths per year from cirrhosis and liver cancer. There is no known cure for chronic HBV, due in part to the continued presence of transcriptionally active DNA in the nucleus which is not directly targeted by current antiviral therapies. Our aim is to inspire and support the discovery of a safe, scalable and effective cure for the benefit of all people living with CHB. Methods: To achieve this, we have created an international research driven forum, the International Coalition to Eliminate Hepatitis B (ICE-HBV), which is coordinating collaborative partnerships among researchers and stakeholders to accelerate the search of an HBV cure. Results: ICE-HBV working groups have developed a joint global scientific strategy for HBV cure, with input from key stakeholders including the HBV affected community. Through engagement with key stakeholders, ICE-HBV aims to drive changes in governmental policy to ensure more funds are channelled to HBV cure research and drug development. ICE-HBV fosters new collaborations among HBV researchers and industry worldwide and initiate new projects to fast-track the discovery of an HBV cure. Conclusions: The push for a cure for chronic HBV infection is particularly timely thanks to the recent development of cell culture infection models that, for the first time, empower truly curative research. Through its global network, ICE-HBV is striving to promote effective collaboration and facilitate opportunities that can produce a cure for chronic HBV infection.
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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.041 | 0.012 |
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".