Peginterferon add‐on results in more HBsAg decline compared to monotherapy in HBeAg‐positive chronic hepatitis B patients
Bibliographic record
Abstract
It is unknown whether peginterferon (PEG-IFN) add-on to entecavir (ETV) leads to more HBsAg decline compared to PEG-IFN monotherapy or combination therapy, and whether ETV therapy may prevent HBsAg increase after PEG-IFN cessation. We performed a post hoc analysis of 396 HBeAg-positive patients treated for 72 weeks with ETV + 24 weeks PEG-IFN add-on from week 24 to 48 (add-on, n = 85), 72 weeks with ETV monotherapy (n = 90), 52 weeks with PEG-IFN monotherapy (n = 111) and 52 weeks PEG-IFN + lamivudine (combination, n = 110) within 2 randomized trials. HBsAg decline was assessed at the end of PEG-IFN (EOP) and 6 months after PEG-IFN (EOF) discontinuation. Differences in baseline characteristics were accounted for using inversed probability of treatment weights. At EOP, a HBsAg reduction of ≥1log10 IU/mL was more frequently achieved for patients in the add-on or combination therapy arms (both 36%), compared to PEG-IFN mono (20%) or ETV (8%) (add-on vs PEG-IFN mono P = 0.050). At EOF, the HBsAg reduction ≥1log10 IU/mL was only sustained in patients treated with ETV consolidation (add-on vs combination and PEG-IFN mono: 40% vs 23% and 18%, P = 0.029 and P = 0.003, respectively). For add-on, combination, PEG-IFN mono and ETV, the mean HBsAg-level change at EOF was -0.84, -0.81, -0.68 and -0.33 log10 IU/mL, respectively (P > 0.05 for PEG-IFN arms). HBeAg loss at EOF was 36%, 31%, 33% and 20%, respectively (P > 0.05). PEG-IFN add-on for 24 weeks results in more on-treatment HBsAg decline than does 52 weeks of PEG-IFN monotherapy. ETV therapy may maintain the HBsAg reduction achieved with PEG-IFN.
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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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".