Adjuvant Fuzheng Huayu Capsule Reduces the Incidence of Hepatocellular Carcinoma in Patients with Hepatitis B‐Caused Cirrhosis
Bibliographic record
Abstract
Aim. Fuzhenghuayu (FZHY) capsule can inhibit the progression of cirrhosis. This study explored whether FZHY can reduce the incidence of hepatocellular carcinoma (HCC) in patients with hepatitis B‐caused cirrhosis (HBC) undergoing antiviral therapy. Methods. A retrospective review of 842 patients with HBC between 2011 and 2015 was performed, including 270 treated with FZHY combined with nucleos (t) ide analogues (NAs) and 572 with NAs alone. The incidence of HCC was compared between the FZHY (n = 259) and control (n = 259) groups using 1 : 1 propensity score (PS) matching. The incidence of HCC in patients with HBC with different Child‐Turcotte‐Pugh (CTP) classifications and Toronto HCC risk index (THRI) scores was analyzed using Kaplan–Meier curves. Results. The 5‐year cumulative incidence of HCC before and after PS matching was 151 (17.9%) and 86 (16.6%), respectively. In PS‐matched samples, the multivariate Cox proportional‐hazards model indicated that the FZHY group demonstrated a significantly lower risk for HCC than the control group (adjusted hazard ratio [aHR] = 0.32, 95% CI 0.19–0.53 P < 0.001). The risk of HCC diminished with increased duration of FZHY use. The stratified analysis revealed that the FZHY group, regardless of CTP classification, benefited significantly from FZHY therapy. Patients in the medium‐ and high‐THRI risk groups were the dominant population for FZHY. Conclusions. FZHY combined with NAs was associated with a significantly lower risk of HCC than NAs alone in patients with HBC, which supports the integration of FZHY with antiviral treatment into clinical practice.
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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 | 0.000 |
| 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".