Synergistic Effect of Biejia-Ruangan on Fibrosis Regression in Patients With Chronic Hepatitis B Treated With Entecavir: A Multicenter, Randomized, Double-Blind, Placebo-Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Long-term nucleos(t)ide analogue (NA) treatment can reverse liver fibrosis in chronic hepatitis B (CHB), but its effect on fibrosis regression remains limited. Biejia-Ruangan (BR) has been approved in China as an antifibrotic traditional Chinese medicine drug in patients with chronic liver diseases. A multicenter randomized controlled trial aims to evaluate the effect of BR on fibrosis regression in CHB patients treated with NAs. METHODS: CHB patients with histologically confirmed advanced fibrosis or cirrhosis were randomly assigned to receive entecavir (ETV) (0.5 mg per day) plus BR (2 g 3 times a day) or placebo for 72 weeks. Liver fibrosis regression was defined as a reduction of ≥ 1 point by the Ishak fibrosis stage (IFS). RESULTS: Overall, 500 patients were enrolled in each group as the intention-to-treat population. The rate of fibrosis regression after 72 weeks of treatment was significantly higher in the ETV + BR group (40% vs 31.8%; P = .0069). Among 388 patients with cirrhosis (ie, IFS ≥ 5) at baseline, the rate of cirrhosis reversal (ie, IFS ≤ 4) was significantly higher in the ETV + BR group (41.5% vs 30.7%; P = .0103). CONCLUSIONS: Addition of BR to the current standard treatment with NAs in CHB patients with advanced fibrosis or cirrhosis can improve liver fibrosis regression. CLINICAL TRIALS REGISTRATION: NCT01965418.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".