Serum hepatitis B virus RNA predicts response to peginterferon treatment in HBeAg‐positive chronic hepatitis B
Bibliographic record
Abstract
Hepatitis B virus (HBV) RNA in serum is a novel biomarker that reflects cccDNA activity. We investigated whether HBV RNA can predict serological response to peginterferon (PEG-IFN) treatment. Serum HBV RNA levels were retrospectively measured at weeks 0, 12, 24 and 52 of therapy and after treatment discontinuation (week 78) in 266 HBeAg-positive chronic HBV patients who had participated in a global randomized controlled trial (HBV99-01 study). Patients received 52 weeks PEG-IFN monotherapy (n = 136) or PEG-IFN and lamivudine (n = 130). The primary end point was HBeAg loss 24 weeks after PEG-IFN discontinuation. At baseline, the mean serum level of HBV RNA was 6.8 (SD 1.2) log c/mL. HBV RNA levels declined to 4.7 (1.7) log c/mL after one year of PEG-IFN therapy alone and to 3.3 (1.2)log c/mL after combination therapy. From week 12 onward, HBV RNA level was significantly lower in patients who achieved HBeAg loss at the end of follow-up as compared to those who did not, regardless of treatment allocation (week 12:4.4 vs 5.1 log c/mL, P = .01; week 24:3.7 vs 4.9 log c/mL, P < .001). The performance of a multivariable model based on HBV RNA level was comparable at week 12 (AUC 0.68) and 24 (AUC 0.72) of therapy. HBV RNA level above 5.5 log c/mL at week 12 showed negative predictive values of 93/67/90/64% for HBV genotypes A/B/C/D for the prediction of HBeAg loss. In conclusion, HBV RNA in serum declines profoundly during PEG-IFN treatment. Early on-treatment HBV RNA level may be used to predict nonresponse.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".