International multicenter validation of <scp>GES</scp> score for <scp>HCC</scp> risk stratification in chronic hepatitis C patients
Bibliographic record
Abstract
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. 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 12,038 patients with chronic HCV were analyzed in this study, of whom 11,202 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 log-rank tests. The performance of the GES score was evaluated using Harrell's C-index (HCI). 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 < .0001), with HCI ranging from 0.55 to 0.76 among all cohorts after adjusting for HCV genotypes and patient ethnicities. 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.
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How this classification was reachedexpand
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".