Hepatitis B Core-Related Antigen Levels Predict Pegylated Interferon-α Therapy Response in HBeAg-Positive Chronic Hepatitis B
Bibliographic record
Abstract
BACKGROUND: Serum hepatitis B core-related antigen (HBcrAg) levels reflect intrahepatic HBV replication activity. We aimed to study whether HBcrAg levels predict response to pegylated interferon (PEG-IFN) treatment in hepatitis B e antigen (HBeAg)-positive chronic hepatitis B (CHB) patients. METHODS: We studied HBcrAg levels in 222 HBeAg-positive patients treated with PEG-IFN with or without lamivudine for 52 weeks in a global randomized trial and compared kinetics across treatment arms and types of response. Optimal HBcrAg cutoffs for stopping therapy were compared to and combined with the currently recommended hepatitis B surface antigen (HBsAg)-based stopping-rules. RESULTS: Baseline HBcrAg levels could not discriminate between responders and non-responders (P=0.91). HBcrAg levels of patients responding to PEG-IFN therapy showed a more pronounced on-treatment decline (mean declines 3.4 versus 1.0 log U/ml; P<0.0001), which was sustained until the end of follow-up (mean declines week 78, 3.8 versus 1.0 log U/ml; P<0.0001). In the PEG-IFN monotherapy group, HBcrAg levels of >8.35 log U/ml at week 24 identified 19 patients (19%) of whom 1 (negative predicitve value [NPV]=95%) achieved a response. The performance of this HBcrAg-based stopping rule alone was not superior to the one based on HBsAg >20,000 IU/ml. Among patients with an HBsAg <20,000 (n=56), 9 (16%) had an HBcrAg >8.35, of whom 8 achieved no response (NPV 89%). CONCLUSIONS: HBeAg-positive CHB patients with a response to PEG-IFN therapy achieve a more pronounced HBcrAg decline. HBcrAg levels at week 24 of therapy could be used to identify non-responders in combination with the established HBsAg-based stopping-rules.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".