Enhanced Liver Fibrosis (ELF) test accurately identifies liver fibrosis in patients with chronic hepatitis C
Bibliographic record
Abstract
Assessment of liver fibrosis is important in determining prognosis and evaluating interventions. Due to limitations of accuracy and patient hazard of liver biopsy, non-invasive methods have been sought to provide information on liver fibrosis, including the European liver fibrosis (ELF) test, shown to have good diagnostic accuracy for the detection of moderate and severe fibrosis. Access to independent cohorts of patients has provided an opportunity to explore if this test could be simplified. This paper reports the simplification of the ELF test and its ability to identity severity of liver fibrosis in external validation studies in patients with chronic hepatitis C (CHC). Paired biopsy and serum samples from 347 naïve patients with CHC in three independent cohorts were analysed. Diagnostic performance characteristics were derived (AUROC, sensitivity and specificity, predictive values), and clinical utility modelling performed to determine the proportion of biopsies that could have been avoided if ELF test was used in this patient group. It was possible to simplify the original ELF test without loss of performance and the new algorithm is reported. The simplified ELF test was able to predict severe fibrosis [pooled AUROC of 0.85 (95% CI 0.81-0.89)] and using clinical utility modelling to predict severe fibrosis (Ishak stages 4-6; METAVIR stages 3 and 4) 81% of biopsies could have been avoided (65% correctly). Issues of spectrum effect in diagnostic test evaluations are discussed. In chronic hepatitis C a simplified ELF test can detect severe liver fibrosis with good accuracy.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.000 |
| 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".