FibroTest and FibroScan for the Prediction of Hepatitis C-Related Fibrosis: A Systematic Review of Diagnostic Test Accuracy
Bibliographic record
Abstract
BACKGROUND: The accurate diagnosis of hepatitis C virus (HCV)-related fibrosis is crucial for prognostication and treatment decisions. Due to the limitations of biopsy, noninvasive alternatives including FibroTest and FibroScan have been developed. Our objective was to systematically review studies describing the accuracy of these tests for predicting HCV-related fibrosis. METHODS: Studies comparing FibroTest or FibroScan versus biopsy in HCV patients were identified via an electronic search. Random effects meta-analyses and areas under summary receiver operating characteristics curves (AUC) were examined to characterize test accuracy for significant fibrosis (F2-4) and cirrhosis. Heterogeneity was explored using meta-regression. RESULTS: Twelve studies were identified, 9 for FibroTest (N = 1,679) and 4 for FibroScan (N = 546). In heterogeneous analyses for significant fibrosis, the AUCs for FibroTest and FibroScan were 0.81 (95% CI 0.78-84) and 0.83 (0.03-1.00), respectively. At a threshold of approximately 0.60, the sensitivity and specificity of the FibroTest were 47% (35-59%) and 90% (87-92%). For FibroScan (threshold approximately 8 kPa), corresponding values were 64% (50-76%) and 87% (80-91%), respectively. Methodological quality, the length of liver biopsy specimens, and inclusion of special populations did not explain the observed heterogeneity. However, the diagnostic accuracy of both measures was associated with the prevalence of significant fibrosis and cirrhosis in the study populations. For cirrhosis, the summary AUCs for FibroTest and FibroScan were 0.90 (95% CI not calculable) and 0.95 (0.87-0.99), respectively. CONCLUSIONS: FibroTest and FibroScan have excellent utility for the identification of HCV-related cirrhosis, but lesser accuracy for earlier stages. Refinements are necessary before these tests can replace liver biopsy.
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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 | 0.023 | 0.100 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.016 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".