Prediction of Hepatocellular Carcinoma and Other Liver‐Related Events in Chronic Hepatitis B Patients With Metabolic Dysfunction or Metabolic Dysfunction‐Associated Steatotic Liver Disease
Bibliographic record
Abstract
INTRODUCTION: Metabolic dysfunction and metabolic dysfunction-associated steatotic liver disease (MASLD) are associated with an increased risk of hepatocellular carcinoma (HCC) in patients with chronic hepatitis B (CHB). We aimed to study risk factors for HCC and to assess the performance of the PAGE-B score in this population. METHODS: We included CHB patients with ≥ 1 metabolic comorbidity from nine centres. Steatosis was diagnosed by ultrasound, CAP, or histology. Risk factors were analysed by Cox regression, and the performance of the PAGE-B score was assessed in the overall population and across relevant subgroups. RESULTS: We included 1922 patients. 1730 (90.0%) were overweight, 434 (22.6%) had hypertension, 254 (13.2%) dyslipidemia, 230 (12.0%) diabetes and 732 (38.1%) MASLD. Presence of cirrhosis, older age, lower platelets and lower albumin were independent risk factors for HCC. The 5-year HCC risk was 0.1%/2.0%/12.4% patients with low/intermediate/high PAGE-B scores (p < 0.001). Consistent results were obtained in patients with MASLD (0/2.8/11.1% for low, intermediate and high PAGE-B scores (p < 0.001)). PAGE-B stratified risk in patients without cirrhosis (0% vs. 1.2% and 1.8%, p < 0.001). Among the subset of patients with cirrhosis, risks were 4.2% (low), 6.9% (intermediate) and 27.3% (high) (p < 0.001). CONCLUSIONS: CHB patients with metabolic dysfunction and/or MASLD are at significant risk of HCC. The PAGE-B score can be used to stratify HCC risk in this population, with negligible 5-year HCC incidence in those without cirrhosis and low PAGE-B scores. However, caution should be exercised in patients with cirrhosis in whom HCC risk remains significant even among those with a low PAGE-B score.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".