Entecavir treatment does not eliminate the risk of hepatocellular carcinoma in chronic hepatitis B: limited role for risk scores in Caucasians
Bibliographic record
Abstract
BACKGROUND: Hepatocellular carcinoma (HCC) risk-scores may predict HCC in Asian entecavir (ETV)-treated patients. We aimed to study risk factors and performance of risk scores during ETV treatment in an ethnically diverse Western population. METHODS: We studied all HBV monoinfected patients treated with ETV from 11 European referral centres within the VIRGIL Network. RESULTS: A total of 744 patients were included; 42% Caucasian, 29% Asian, 19% other, 10% unknown. At baseline, 164 patients (22%) had cirrhosis. During a median follow-up of 167 (IQR 82-212) weeks, 14 patients developed HCC of whom nine (64%) had cirrhosis at baseline. The 5-year cumulative incidence rate of HCC was 2.1% for non-cirrhotic and 10.9% for cirrhotic patients (p<0.001). HCC incidence was higher in older patients (p<0.001) and patients with lower baseline platelet counts (p=0.02). Twelve patients who developed HCC achieved virologic response (HBV DNA <80 IU/mL) before HCC. At baseline, higher CU-HCC and GAG-HCC, but not REACH-B scores were associated with development of HCC. Discriminatory performance of HCC risk scores was low, with sensitivity ranging from 18% to 73%, and c-statistics from 0.71 to 0.85. Performance was further reduced in Caucasians with c-statistics from 0.54 to 0.74. Predicted risk of HCC based on risk-scores declined during ETV therapy (all p<0.001), but predictive performances after 1 year were comparable to those at baseline. CONCLUSIONS: Cumulative incidence of HCC is low in patients treated with ETV, but ETV does not eliminate the risk of HCC. Discriminatory performance of HCC risk scores was limited, particularly in Caucasians, at baseline and during therapy.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".