Stratification of hepatocellular carcinoma risk in primary biliary cirrhosis: a multicentre international study
Bibliographic record
Abstract
OBJECTIVE: Hepatocellular carcinoma (HCC) is an infrequent yet critical event in primary biliary cirrhosis (PBC); however, predictive tools remain ill-defined. Our objective was to identify candidate risk factors for HCC development in patients with PBC. DESIGN: Risk factor analysis was performed in over 15 centres from North America and Europe spanning >40 years observation period using Cox proportional hazards assumptions, logistic regression, and Kaplan-Meier estimates. RESULTS: Of 4565 patients with PBC 123 developed HCC, yielding an incidence rate (IR) of 3.4 cases/1000 patient-years. HCC was significantly more common in men (p<0.0001), and on univariate analysis factors at PBC diagnosis associated with future HCC development were male sex (unadjusted HR 2.91, p<0.0001), elevated serum aspartate transaminase (HR 1.24, p<0.0001), advanced disease (HR 2.72, p=0.022), thrombocytopenia (HR 1.65, p<0.0001), and hepatic decompensation (HR 9.89, p<0.0001). As such, non-treatment with ursodeoxycholic acid itself was not associated with cancer development; however, 12-month stratification by biochemical non-response (Paris-I criteria) associated significantly with future risk of HCC (HR 4.52, p<0.0001; IR 6.6 vs 1.4, p<0.0001). Non-response predicted future risk in patients with early stage disease (IR 4.7 vs 1.2, p=0.005), advanced disease (HR 2.79, p=0.02; IR 11.2 vs 4.4, p=0.033), and when restricting the analysis to only male patients (HR 4.44, p<0.001; IR 18.2 vs 5.4, p<0.001). On multivariable analysis biochemical non-response remained the most significant factor predictive of future HCC risk (adjusted HR 3.44, p<0.0001). CONCLUSIONS: This uniquely powered, internationally representative cohort robustly demonstrates that 12-month biochemical non-response is associated with increased future risk of developing HCC in PBC. Such risk stratification is relevant to patient care and development of new therapies.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".