Addition of PEG-interferon to long-term nucleos(t)ide analogue therapy enhances HBsAg decline and clearance in HBeAg-negative chronic hepatitis B: Multicentre Randomized Trial (PAS Study)
Bibliographic record
Abstract
We studied whether 48 weeks of PEG-IFN alfa-2a add-on increases HBsAg-decline and clearance in HBeAg-negative patients on long-term nucleo(s)tide analogue (NA) therapy. In this investigator-initiated, randomized, controlled trial conducted in Europe and Canada, HBeAg-negative patients treated with NA > 12 months, with HBVDNA < 200 IU/mL, were enrolled. Patients were randomized 2:1 to 48 weeks of PEG-IFN alfa-2a add-on (180 μg per week) or continued NA-monotherapy with subsequent follow-up to Week 72. Endpoints were HBsAg decline (≥1 log10 IU/mL) and HBsAg clearance at Week 48. Of the 86 patients in the modified-intention-to-treat analysis, 58 patients received PEG-IFN add-on, and 28 continued NA monotherapy. At Week 48, 16(28%) patients achieved HBsAg decline ≥1 log10 in the add-on arm versus none on NA-monotherapy (p <.001), and HBsAg clearance was observed in 6 (10%) PEG-IFN add-on patients versus 0% NA-monotherapy (p =.01). HBVRNA was only detected in 2% after PEG-IFN treatment versus 19% in NA-monotherapy (p =.002) at Week 48. PEG-IFN add-on therapy was well tolerated in majority of patients. Low baseline HBsAg levels (<10 IU/mL) identified patients most likely to achieve HBsAg loss with PEG-IFN add-on, whereas an HBsAg level > 200 IU/mL at on-treatment Week 12 was highly predictive of non-response (NPV = 100%). Addition of PEG-IFN to long-term NA enhanced HBsAg decline and increased the chance of HBsAg clearance in HBeAg-negative patients on long-term NA. On-treatment HBsAg levels >200 IU/mL identify patients unlikely to benefit from PEG-IFN add-on and could be used as a potential stopping-rule for PEG-IFN therapy. Our findings support further exploration of immune modulation add-on to antiviral therapy, preferably using response-guided strategies, to increase functional cure rates in patients with CHB.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 |
| 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 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".