HBsAg decline and clearance with peg-IFN therapy added to nucleos(t)ide analogues: an individual participant data meta-analysis of prospective trials (PROSPER)
Bibliographic record
Abstract
BACKGROUND: Peg-interferon (peg-IFN) plays an increasingly important role in HBV cure strategies, either in combination with novel antivirals, as a lead-in or as consolidation treatment. OBJECTIVE: We aimed to provide estimates of hepatitis B surface antigen (HBsAg) decline and clearance that can be achieved with peg-IFN addition to nucleos(t)ide analogue (NA) therapy. DESIGN: This is a post hoc meta-analysis of individual participant data from eight clinical trials involving chronic hepatitis B patients on NA therapy who received peg-IFN add-on. The primary endpoint was HBsAg loss at end of follow-up (EOF, 6-12 months after end of peg-IFN). Secondary analyses focused on HBsAg decline. RESULTS: 581 patients were included. At the start of peg-IFN therapy (SOT), 44% were hepatitis B envelope antigen (HBeAg) positive, mean HBsAg level was 3.03 log10 IU/mL (HBsAg<100: 12%; 100-1000: 28%; ≥1000: 60%), and planned duration of peg-IFN was 48 weeks in 496 patients (85%).At EOF, 50 (8.6%) patients achieved HBsAg loss (HBsAg<100/100-1000/≥1000: 37.7/9.8/2.3%, p<0.001) Findings were consistent across ethnicities (Caucasian: 30.0/8.7/3.6%; Asian: 39.3/9.2/2.2%). In patients with SOT HBsAg≥1000 IU/mL, levels <1000 and <100 were achieved in 29.7% and 8.9% at 24 weeks and in 47.5% and 16.3% at 48 weeks of peg-IFN therapy, respectively. CONCLUSION: Peg-IFN add-on results in HBsAg loss in 18% of patients with SOT HBsAg<1000 IU/mL, and in 38% if SOT HBsAg<100 IU/mL. Among patients with higher HBsAg levels, peg-IFN could be used to reduce HBsAg to below thresholds associated with response to novel compounds.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 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 | 0.000 |
| 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".