International Multicenter Validation of GES Score for HCC Risk Stratification in Chronic Hepatitis C Patients
Bibliographic record
Abstract
Background and Aims: We have recently demonstrated the ability of a simple predictive model (GES) score to determine the risk of hepatocellular carcinoma (HCC) after using direct-acting antivirals. However, our results were restricted to Egyptian patients with hepatitis C virus (HCV) genotype 4. Therefore, we studied a large, independent cohort of multiethnic populations through our international collaborative activity. Methods: Depending on their GES scores, patients are stratified into low risk (≤ 6/12.5), intermediate risk (> 6–7.5/12.5), and high risk (> 7.5/12.5) for HCC. A total of 12038 patients with chronic HCV were analyzed in this study, of whom 11202 were recruited from 54 centers in France, Japan, India, the U.S., and Spain, and the remaining 836 were selected from the Gilead-sponsored randomized controlled trial conducted across the U.S., Europe, Canada, and Australia. Descriptive statistics and survival analysis were performed using Kaplan–Meier and log-rank tests. The performance of the GES score was evaluated using Harrell's C-index (HCI). Results : The GES score proved successful at stratifying all patients into 3 risk groups, namely low-risk, intermediate-risk, and high-risk. It also displayed significant predictive value for HCC development in all participants (P < 0 .0001), with HCI ranging from 0.55 to 0.76 among all cohorts after adjusting for HCV genotypes and patient ethnicities. Conclusion: The GES score can be used to stratify HCV patients into 3 categories of risk for HCC, namely low-risk, intermediate-risk, and high-risk, irrespective of their ethnicities or HCV genotypes. This international multicenter validation may allow the use of GES score in individualized HCC risk-based surveillance programs.Funding: None to declare. Declaration of Interest: None to declare. Ethical Approval: The protocol was approved by the Institutional Research Board of each participating center, as per local regulations
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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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".