Prediction of long‐term clinical outcome in a diverse chronic hepatitis B population: Role of the PAGE‐B score
Bibliographic record
Abstract
An abundance of noninvasive scores have been associated with fibrosis and hepatocellular carcinoma (HCC) development. We aimed to compare the prognostic ability of these scores in relation to liver histology in chronic hepatitis B (CHB) patients. Liver biopsies from treatment-naïve CHB patients at one tertiary care centre were scored by a single hepato-pathologist. Laboratory values at liver biopsy were used to calculate the PAGE-B, REACH-B, GAG-HCC, CU-HCC and FIB-4 scores. Any clinical event was defined as HCC development, liver failure, transplantation and mortality. HCC and mortality data were obtained from national database registries. Of 557 patients, 40 developed a clinical event within a median follow-up of 10.1 (IQR 5.7-15.9) years. The PAGE-B score predicted any clinical event (C-statistic.86, 95% CI: 0.80-0.92), HCC development (C-statistic .91) and reduced transplant-free survival (C-statistic .83) with good accuracy, also when stratified by ethnicity, antiviral therapy after biopsy or advanced fibrosis. The C-statistics (95% CI) of the REACH-B, GAG-HCC, CU-HCC and FIB-4 scores for any event were .70 (0.59-0.81), .82 (0.75-0.89), .73 (0.63-0.84) and.79 (0.69-0.89), respectively. The PAGE-B event risk assessment improved modestly when combined with the Ishak fibrosis stage (C-statistic .87, 95% CI: 0.82-0.93). The PAGE-B score showed the best performance in assessing the likelihood of developing a clinical event among a diverse CHB population over 15 years of follow-up. Additional liver histological characteristics did not appear to provide a clinically significant improvement.
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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 | 0.005 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".