Can we use HCC risk scores to individualize surveillance in chronic hepatitis B infection?
Bibliographic record
Abstract
Chronic hepatitis B is one of the leading causes of hepatocellular carcinoma (HCC) worldwide. Accurate prediction of HCC risk is important for decisions on antiviral therapy and HCC surveillance. In the last few years, a number of Asian groups have derived and validated several HCC risk scores based on well-known risk factors such as cirrhosis, age, male sex and high viral load. Overall, these scores have high negative predictive values of over 95% in excluding HCC development in 3 to 10 years. The REACH-B score was derived from a community cohort of non-cirrhotic patients and is better applied in the primary care setting. In contrast, the GAG-HCC and CU-HCC scores were derived from hospital cohorts and include cirrhosis as a major integral component. While the latter scores may be more applicable to patients at specialist clinics, the diagnosis of cirrhosis based on routine imaging and clinical parameters can be inaccurate. To this end, recent developments in non-invasive tests of liver fibrosis may further refine the risk prediction. The application of HCC risk scores in patients on antiviral therapy and in other ethnic groups should be evaluated in future studies.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".