Research priorities for the discovery of a cure for chronic hepatitis B: Report of a workshop
Bibliographic record
Abstract
In early 2017, the Hepatitis B Foundation invited 30 experts in the fields of hepatitis B and liver cancer research to identify projects they deemed important to the goal of finding a cure for chronic hepatitis B and D and the diseases with which these viral infections are associated. They were also asked to identify general categories of research and to prioritize sub-project topics within those areas. The experts generally agreed on broadly defined areas of research, but there was usually little difference between the highest and lowest scoring projects; for the most part, all programs described in this document were considered valuable and necessary. An executive summary of this discussion was recently published (Alter et al., Hepatology 2017). The present manuscript reports the areas of research identified by the workshop participants, provides a brief rationale for their selection, and attempts to express differences among the priorities assigned to each area of research, when such distinctions were expressed.
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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 | 0.073 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.011 | 0.019 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".