Estimation of stage‐specific fibrosis progression rates in chronic hepatitis C virus infection
Bibliographic record
Abstract
UNLABELLED: Published estimates of liver fibrosis progression in individuals with chronic hepatitis C virus (HCV) infection are heterogeneous. We aimed to estimate stage-specific fibrosis progression rates and their determinants in these individuals. A systematic review of published prognostic studies was undertaken. Study inclusion criteria were as follows: (1) presence of HCV infection determined by serological assays; (2) available information about age at assessment of liver disease or HCV acquisition; (3) duration of HCV infection; and (4) histological and/or clinical diagnosis of cirrhosis. Annual stage-specific transition probabilities (F0-->F1, ... , F3-->F4) were derived using the Markov maximum likelihood estimation method and a meta-analysis was performed. The impact of potential covariates was evaluated using meta-regression. A total of 111 studies of individuals with chronic HCV infection (n = 33,121) were included. Based on the random effects model, the estimated annual mean (95% confidence interval) stage-specific transition probabilities were: F0-->F1 0.117 (0.104-0.130); F1-->F2 0.085 (0.075-0.096); F2-->F3 0.120 (0.109-0.133); and F3-->F4 0.116 (0.104-0.129). The estimated prevalence of cirrhosis at 20 years after the infection was 16% (14%-19%) for all studies, 18% (15%-21%) for cross-sectional/retrospective studies, 7% (4%-14%) for retrospective-prospective studies, 18% (16%-21%) for studies conducted in clinical settings, and 7% (4%-12%) for studies conducted in nonclinical settings. Duration of infection was the most consistent factor significantly associated with progression of fibrosis. CONCLUSION: Our large systematic review provides increased precision in estimating fibrosis progression in chronic HCV infection and supports nonlinear disease progression. Estimates of progression to cirrhosis from studies conducted in clinical settings were lower than previous estimates.
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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 | 0.024 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.012 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".