Long-Term Entecavir Therapy Results in the Reversal of Fibrosis/Cirrhosis and Continued Histological Improvement in Patients with Chronic Hepatitis B†,‡
Bibliographic record
Abstract
UNLABELLED: One year of treatment with entecavir (0.5 mg daily) in nucleoside-naive patients with hepatitis B e antigen (HBeAg)-positive or HBeAg-negative chronic hepatitis B (CHB) resulted in significantly improved liver histology and virological and biochemical endpoints in comparison with lamivudine. Patients who received at least 3 years of cumulative entecavir therapy in phase 3 studies and a long-term rollover study and underwent long-term liver biopsy were evaluated for improvements in histological appearance. Sixty-nine patients [50 HBeAg-positive and 19 HBeAg-negative] receiving entecavir therapy underwent long-term liver biopsy (median time of biopsy = 6 years, range = 3-7 years). Histological improvement was analyzed for 57 patients who had adequate baseline biopsy samples, baseline Knodell necroinflammatory scores > or =2, and adequate long-term biopsy samples. At the time of long-term biopsy, all patients in the cohort had a hepatitis B virus DNA level <300 copies/mL, and 86% had a normalized alanine aminotransferase level. Histological improvement (> or =2-point decrease in the Knodell necroinflammatory score and no worsening of the Knodell fibrosis score) was observed in 96% of patients, and a > or =1-point improvement in the Ishak fibrosis score was found in 88% of patients, including all 10 patients with advanced fibrosis or cirrhosis at the phase 3 baseline. CONCLUSION: The majority of nucleoside-naive patients with CHB who were treated with entecavir in this long-term cohort achieved substantial histological improvement and regression of fibrosis or cirrhosis.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".