Immunological biomarker discovery in cure regimens for chronic hepatitis B virus infection
Bibliographic record
Abstract
There have been unprecedented advances in the identification of new treatment targets for chronic hepatitis B that are being developed with the goal of achieving functional cure in patients who would otherwise require lifelong nucleoside analogue treatment. Many of the new investigational therapies either directly target the immune system or are anticipated to impact immunity indirectly through modulation of the viral lifecycle and antigen production. While new viral biomarkers (HBV RNA, HBcAg, small, middle, large HBs isoforms) are proceeding through validation steps in clinical studies, immunological biomarkers are non-existent outside of clinical assays for antibodies to HBs, HBc and HBe. To develop clinically applicable immunological biomarkers to measure mechanisms of action, inform logical combination strategies, and guide clinical management for use and discontinuation of immune-targeting drugs, immune assays must be incorporated into phase I/II clinical trials. This paper will discuss the importance of sample collection, the assays available for immunological analyses, their advantages/disadvantages and suggestions for their implementation in clinical trials. Careful consideration must be given to ensure appropriate immunological studies are included as a primary component of the trial with deeper immunological analysis provided by ancillary studies. Standardising immunological assays and data obtained from clinical trials will identify biomarkers that can be deployed in the clinic, independently of specialised immunology laboratories.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".