Successful Treatment with Peginterferon alfa-2b of HBeAg-positive HBV Non-Responders to Standard Interferon or Lamivudine
Bibliographic record
Abstract
OBJECTIVES Antiviral therapy leads to HBeAg seroconversion in 10–40% of the patients with HBeAg-positive chronic hepatitis B. Nonresponse may result in progression of liver disease and increased risk of hepatocellular carcinoma. As part of a global randomized controlled trial we investigated the efficacy (i.e., loss of HBeAg at the end of follow-up) of peginterferon alfa-2b (Peg-IFN α2b) in patients who failed to respond to previous courses of standard interferon (IFN) or lamivudine. METHODS We analyzed a total of 76 previous nonresponders: 37 were nonresponders to standard IFN, 17 were nonresponders to lamivudine, and 22 were nonresponders to both therapies. All patients received a 52-wks course of 100 μg Peg-IFN α2b weekly combined with either 100 mg lamivudine daily or a placebo. After therapy patients were followed for 26 wks. RESULTS Thirteen (35%) nonresponders to previous IFN, five (29%) nonresponders to previous lamivudine, and four (22%) nonresponders to both IFN and lamivudine responded to treatment with Peg-IFN α2b. No difference in response was found for those treated with Peg-IFN α2b alone or in combination with lamivudine. Nonresponders to prior IFN therapy with baseline ALT (alanine aminotransferase) > 4 × ULN (upper limit of normal) responded better to Peg-IFN α2b than those with ALT levels ≤ 4 × ULN (53% vs 20%, respectively, p = 0.036). CONCLUSIONS Peg-IFN α2b is effective in approximately one-third of patients who failed to respond to previous treatment with standard IFN or lamivudine. High serum ALT level at baseline of Peg-IFN α2b therapy was the best predictor for response in these patients.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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 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".