Risk of hepatocellular carcinoma in chronic hepatitis B: Assessment and modification with current antiviral therapy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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 it