Hepatitis B virus haplotype number at baseline is a predictive marker of functional cure during antiviral therapy for patients with genotypes A and D <scp>HBeAg</scp>‐positive chronic hepatitis B
Bibliographic record
Abstract
BACKGROUNDS AND AIMS: We investigated associations between hepatitis B virus (HBV) genome-length haplotype number (HN) at baseline in subjects with HBeAg-positive chronic hepatitis B (CHB), and the likelihood of achieving functional cure during direct-acting antiviral therapy METHOD: We analysed 86 HBeAg-positive baseline samples from patients with HBV genotypes A and D who were enrolled in a Phase II trial of tenofovir disoproxil fumarate (TDF) to determine if HN was a biomarker of HBsAg loss during therapy. Findings were validated using baseline samples from 181 patients with HBV genotypes A and D from an independent clinical trial utilising TDF or tenofovir alafenamide therapy in HBeAg-positive CHB. RESULTS: In the HBeAg-positive test cohort, patients with genotypes A or D and ≤2 haplotypes had a minimum of 21-fold higher likelihood of achieving HBsAg loss on TDF. Baseline HN (p < 0.0001) was a stronger predictor of HBsAg loss on therapy than HBsAg titre (p = 0.03), HBeAg titre (p = 0.0002), or the presence of HBV basal core promoter (A1762T, p = 0.0379 and G1764A, p = 0.0176) or G1896A precore mutations (p = 0.0218). This finding was validated in the independent validation cohort. HN was statistically higher in patients with HBV genotypes B or C infection compared to genotypes A and D. CONCLUSION: Baseline HN ≤2 predicts which patients with HBV genotypes A or D will more likely progress to functional cure on current direct-acting antiviral therapy, with greater accuracy than current biomarkers including baseline HBsAg and HBeAg titre.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".