Presence of precore and core promoter mutants limits the probability of response to peginterferon in hepatitis B e antigen-positive chronic hepatitis B
Bibliographic record
Abstract
UNLABELLED: Peginterferon (PEG-IFN) treatment of hepatitis B e antigen (HBeAg)-positive chronic hepatitis B (CHB) results in HBeAg loss in 30% of patients, but clearance of hepatitis B virus (HBV) DNA and hepatitis B surface antigen (HBsAg) from serum is less often achieved. We investigated whether the presence of precore (PC) and basal core promoter (BCP) mutants before PEG-IFN treatment affects serological and virological response. A total of 214 HBeAg-positive CHB patients treated with PEG-IFN ± lamivudine for 52 weeks in a global randomized trial were classified at baseline as wildtype (WT) or non-WT (detectable mutants at PC/BCP) by line-probe assay. Response was assessed at 6 months posttreatment and through long-term follow-up (LTFU). Mutants were detected in 64% of patients, in varying frequencies across HBV genotypes A through D. Patients with WT had higher baseline HBV DNA, HBeAg, and HBsAg levels than patients with non-WT. Patients with WT were more likely to achieve HBeAg loss with HBV DNA <10,000 copies/mL (response, 34 versus 11%, P < 0.001) and HBsAg clearance (18 versus 2%, P < 0.001) at week 78 than non-WT patients. Among WT patients who achieved HBeAg clearance at week 78, 78% had undetectable HBV DNA and 61% achieved HBsAg clearance at LTFU (versus 26% and 15% in non-WT patients, P < 0.001 for both). The presence of WT virus at baseline was an independent predictor of response (odds ratio [OR] 2.90, 95% confidence interval [CI]: 1.15-7.31, P = 0.023) and HBsAg clearance (OR 5.58, 95% CI: 1.26-24.63, P = 0.013) and patients with non-A genotypes with detectable mutants had a low probability of response. CONCLUSION: The presence of only WT virus at baseline is a strong predictor of response (HBeAg loss with HBV DNA <10,000 copies/mL) to PEG-IFN for HBeAg-positive CHB. Patients with detectable PC and/or BCP mutants have a lower probability of response and are less optimal candidates for PEG-IFN therapy.
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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 | 0.001 | 0.001 |
| 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".