Risk of hepatocellular carcinoma in chronic hepatitis B: Assessment and modification with current antiviral therapy
Bibliographic record
Abstract
In the treatment of chronic hepatitis B (CHB), the ultimate goal is preventing hepatitis B virus (HBV)-associated liver disease, including hepatocellular carcinoma (HCC). Recently published studies show that in CHB patients treated with the currently recommended first-line nucleos(t)ide analogs (NAs) entecavir or tenofovir, annual HCC incidences range from 0.01% to 1.4% in non-cirrhotic patients, and from 0.9% to 5.4% in those with cirrhosis. In Asian studies including matched untreated controls, current NA therapy consistently resulted in a significantly lower HCC incidence in patients with cirrhosis, amounting to an overall HCC risk reduction of ∼30%; in non-cirrhotic patients, HCC risk reduction was overall ∼80%, but this was only observed in some studies. For patients of Caucasian origin, no appropriate comparative studies are available to date to evaluate the impact of NA treatment on HCC. Achievement of a virologic response under current NA therapy was associated with a lower HCC risk in Asian, but not Caucasian studies. Studies comparing entecavir or tenofovir with older NAs generally found no difference in HCC risk reduction between agents, except for one study which used no rescue therapy in patients developing lamivudine resistance. Overall, these data indicate that with the current, potent NAs, HCC risk can be reduced but not eliminated, probably due to risk factors that are not amenable to change by antiviral therapy, or events that may have taken place before treatment initiation. Validated pre- and on-therapy HCC risk calculators that inform the best practice for HCC surveillance and facilitate patient counseling would be of great practical value.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".