The applicability of hepatocellular carcinoma risk prediction scores in a North American patient population with chronic hepatitis B infection
Bibliographic record
Abstract
BACKGROUND: Patients with chronic hepatitis B (CHB) infection are at an increased risk of developing hepatocellular carcinoma (HCC). Risk scores have been developed in Asian populations to predict HCC risk over time. AIM: To assess the performance of HCC risk prediction models in a heterogeneous population of patients with CHB. METHODS: Scores were calculated at baseline using CU-HCC, REACH-B, NGM1-HCC, NGM2-HCC and GAG-HCC models and the incidence of HCC was determined. The predictive ability of each score was evaluated using the area under the receiver operating characteristic curve (AUROC), Cox regression and plots of observed versus predicted HCC. The predictive value of the scores was compared between Asian and non-Asian patients and between cirrhotic versus non-cirrhotic with and without treatment. RESULTS: Of 2105 patients, 70 developed HCC. Increasing risk score was associated with HCC in all models. The CU-HCC model had the highest AUROC in Asian (0.85) and non-Asian (0.91) patients. Patients identified as low risk by any model had a very low incidence of HCC (0-0.15 per year), with the highest proportion of patients identified as low risk using CU-HCC (67%) or GAG-HCC (78%). The risk of HCC was similar to predicted for low-risk and medium-risk patients but was lower than predicted for high-risk patients. Treated patients had a lower than predicted risk of HCC, particularly in non-cirrhotic high-risk patients with longer follow-up. CONCLUSIONS: Although all models predicted the risk of HCC, models that incorporated parameters of liver function or cirrhosis (CU-HCC/GAG-HCC) were most accurate. Low-risk patients likely require reduced HCC surveillance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.007 |
| 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 | 0.000 |
| Research integrity | 0.000 | 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".