Impact of long‐term tenofovir disoproxil fumarate on incidence of hepatocellular carcinoma in patients with chronic hepatitis B
Bibliographic record
Abstract
BACKGROUND: Efficacy trials have shown that antiviral therapy improves the outcomes of patients with chronic hepatitis B virus (HBV) infection. However, prospective data regarding the effect of antiviral therapy on the incidence of hepatocellular carcinoma (HCC), especially among patients without cirrhosis, are limited. The authors examined the impact of tenofovir disoproxil fumarate (TDF) on the incidence of HCC using a validated prediction model. METHODS: The incidence of HCC in patients treated with TDF was obtained in the pivotal TDF registration studies after 384 weeks of follow-up. The predicted risk of HCC in individual patients was calculated using the Risk Estimation for Hepatocellular Carcinoma in Chronic Hepatitis B (REACH-B) model, which estimates HCC incidence for up to 10 years based on age, sex, alanine aminotransferase level, hepatitis B e antigen status, and HBV-DNA. Standardized incidence ratios (SIRs) were calculated comparing the observed and predicted numbers of HCC cases in the study cohort. RESULTS: Among 634 patients with evaluable baseline biopsies, 152 had cirrhosis (Ishak fibrosis score of 5 or 6) and 482 did not. During the 384 weeks of study, 14 cases of HCC were reported, with 4 occurring within the first year. The incidence of HCC was 0.37% per year in the study as a whole (0.28% among patients without cirrhosis and 0.65% among patients with cirrhosis). Among patients without cirrhosis, the observed incidence of HCC was significantly lower than predicted (SIR, 0.40; 95% confidence interval, 0.199-0.795). The last HCC case in a patient with cirrhosis occurred around week 192 with an SIR of 0.51 (95% confidence interval, 0.231-1.144) reported at week 384. CONCLUSIONS: Based on the REACH-B risk calculator, long-term therapy with TDF was associated with a reduced incidence of HCC among patients without cirrhosis who met treatment criteria.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| 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".