Presence of Anti-Interferon Antibodies is Not Associated with Non-Response to Pegylated Interferon Treatment in Chronic Hepatitis B
Bibliographic record
Abstract
BACKGROUND: Several factors have been related to response to pegylated interferon (PEG-IFN) in chronic hepatitis B (CHB). The occurrence of anti-IFN antibodies is associated with non-response to PEG-IFN in chronic hepatitis C. This study investigated the association between anti-IFN antibodies and response to PEG-IFN in CHB. METHODS: Presence of anti-IFN antibodies was assessed at baseline and at 3 and 6 months post-treatment in 323 CHB patients treated with PEG-IFN for 1 year. RESULTS: At baseline, anti-IFN antibodies were detected in 112 (35%) patients. Prevalence was higher in HBeAg-negative compared to HBeAg-positive CHB (43% versus 31%, respectively; P=0.03). Detection of anti-IFN antibodies was not associated with age, sex or HBV genotype. Presence of anti-IFN antibodies at baseline was associated with previous IFN therapy failure (P=0.04), which remained after adjustment for HBeAg status (OR 2.0, 95% CI 1.1, 3.7; P=0.03). Presence of anti-IFN antibodies at baseline was not associated with response, nor with HBV DNA or HBsAg decline (all P-values>0.3). Overall, 56 of 211 (27%) patients without anti-IFN at baseline developed anti-IFN antibodies after PEG-IFN treatment. Response rates did not differ between patients who developed anti-IFN antibodies and patients who did not develop anti-IFN antibodies during treatment (P=0.1). CONCLUSIONS: Anti-IFN antibodies may frequently be detected in CHB patients, and presence is associated with previous IFN therapy. However, presence or development of anti-IFN antibodies after PEG-IFN therapy is not associated with non-response to PEG-IFN treatment in CHB. There appears to be no future role for anti-IFN antibodies in predicting response to PEG-IFN in 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".