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 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| 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".