Progression of the FIB-4 index among patients with chronic HCV infection and early liver disease
Bibliographic record
Abstract
BACKGROUND AND AIMS: Historical paired liver biopsy studies are likely to underestimate current progression of disease in patients with chronic hepatitis C virus (HCV) infection. We aimed to assess liver disease progression according to the non-invasive Fibrosis-4 (FIB-4) index in patients with chronic HCV and early disease. METHODS AND RESULTS: Patients diagnosed with chronic HCV and FIB-4 <3.25 from four international liver clinics were included in a retrospective cohort study. Follow-up ended at start of antiviral therapy resulting in sustained virological response, at time of liver transplantation or death. Primary outcome of advanced liver disease was defined as FIB-4 >3.25 during follow-up. Survival analyses were used to assess time to FIB-4 >3.25.In total, 4286 patients were followed for a median of 5.0 (IQR 1.7-9.4) years, during which 41 071 FIB-4 measurements were collected. At baseline, median age was 47 (IQR 39-55) years, 2529 (59.0%) were male, and 2787 (65.0%) patients had a FIB-4 <1.45. Advanced liver disease developed in 821 patients. Overall, 10-year cumulative incidence of advanced disease was 32.1% (95% CI 29.9% to 34.3%). Patients who developed advanced disease showed an exponential FIB-4 increase. Among patients with a presumed date of HCV infection, cumulative incidence of advanced disease increased 7.7-fold from 20 to 40 years as opposed to the first 20 years after HCV infection. CONCLUSIONS: The rate of advanced liver disease is high among chronic HCV-infected patients with early disease at time of diagnosis, among whom liver disease progression accelerated over time. These results emphasise the need to overcome any limitations with respect to diagnosing and treating all patients with chronic HCV across the globe.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| 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".