Randomized Trial of Tenofovir With or Without Peginterferon Alfa Followed by Protocolized Treatment Withdrawal in Adults With Chronic Hepatitis B
Bibliographic record
Abstract
INTRODUCTION: Hepatitis B surface antigen (HBsAg) loss is associated with improved long-term outcomes of patients with chronic hepatitis B but is infrequently achieved with current monotherapies. We assessed whether combination strategies that included treatment withdrawal enhanced HBsAg loss. METHODS: A randomized (1:1) trial of tenofovir disoproxil fumarate (TDF) for 192 weeks with or without peginterferon (PegIFN) alfa-2a for the first 24 weeks, followed by withdrawal of TDF at week 192 with 48 weeks of off-treatment follow-up to week 240. The primary end point was HBsAg loss at week 240. RESULTS: Of 201 participants (52% HBeAg positive, 12%/6% genotype A/A2, 7% cirrhosis) randomized to TDF + PegIFN (n = 102) or TDF alone (n = 99), 6 participants had lost HBsAg at the end of the treatment phase (week 192), 5 (5.3%) in the combination group, and 1 (1.0%) in the TDF alone group ( P = 0.09). By week 240, 9 participants had cleared HBsAg, 5.3% in combination, and 4.1% in monotherapy arms ( P = 0.73). HBsAg decline and loss occurred earlier with TDF + PegIFN than TDF, with a ≥1-logIU/mL qHBsAg decline by week 24 in 28% in TDF + PegIFN compared with 6% in TDF ( P = 0.04). HBsAg loss occurred in 7 of 12 (58%) with hepatitis B virus subgenotype A2 (all HBeAg positive) compared with only 2 of 189 (1%) with other hepatitis B virus genotypes and in 8 of 93 (8.6%) HBeAg positive vs 1 of 87 (1.1%) HBeAg negative. DISCUSSION: PegIFN combined TDF followed by protocolized TDF withdrawal led to earlier but not higher percentages of HBsAg clearance. Pretreatment HBeAg positivity and subgenotype A2 were strongly associated with HBsAg clearance.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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 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".