Hepatitis B Virus RNA as Early Predictor for Response to Pegylated Interferon Alpha in HBeAg-Negative Chronic Hepatitis B
Bibliographic record
Abstract
BACKGROUND: Hepatitis B virus RNA (HBV-RNA) is a novel serum biomarker that correlates with transcription of intrahepatic covalently closed circular (cccDNA), which is an important target for pegylated interferon (PEG-IFN) and novel therapies for functional cure. We studied HBV-RNA kinetics following PEG-IFN treatment and its potential role as a predictor to response in HBeAg-negative chronic hepatitis B (CHB) patients. METHODS: HBV-RNA levels were measured in 133 HBeAg-negative CHB patients treated in an international randomized controlled trial (PARC study). Patients received PEG-IFN α-2a for 48 weeks. HBV-RNA was measured from baseline through week 144. Response was defined as HBV-DNA <2000 IU/mL and ALT normalization at week 72. Kinetics of HBV-RNA were compared with HBV-DNA, HBsAg, and HBcrAg. RESULTS: Mean HBV-RNA at baseline was 4.4 (standard deviation [SD] 1.2) log10 c/mL. At week 12, HBV-RNA declined by -1.6 (1.1) log10 c/mL. HBV-RNA showed a greater decline in responders compared to nonresponders early at week 12 (-2.0 [1.2] vs -1.5 [1.1] log10 c/mL, P = .04). HBV-RNA level above 1700 c/mL (3.2 log10 c/mL) had a negative predictive value of 91% at week 12 and 93% at week 24 (P = .01) for response. Overall, HBV-RNA showed a stronger correlation with HBV-DNA and HBcrAg (.82 and .80, P < .001) and a weak correlation with HBsAg (.25). At week 12, HBV-RNA was significantly lower among patients with lower HBsAg (<100 IU/mL) or HBsAg loss at week 144. CONCLUSIONS: During PEG-IFN treatment for HBeAg-negative CHB, HBV-RNA showed a fast and significant decline that correlates with treatment response and HBsAg loss at long-term follow-up. CLINICAL TRIALS REGISTRATION: NCT00114361.
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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 | 0.000 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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) | 0.000 | 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 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".