Systematic review: Accuracy of the enhanced liver fibrosis test for diagnosing advanced liver fibrosis and cirrhosis
Bibliographic record
Abstract
BACKGROUND AND AIMS: The rising incidence of chronic liver disease (CLD) has increased the need for early recognition. This systematic review assesses the diagnostic accuracy of the enhanced liver fibrosis (ELF) test in cases of advanced fibrosis and cirrhosis due to multiple etiologies in at-risk populations. METHODS: Studies evaluating the ELF accuracy in identifying advanced fibrosis or cirrhosis, defined as METAVIR stage F ≥ 3 and F = 4 or equivalent, in patients with non-alcoholic fatty liver disease (NAFLD), alcohol liver disease (ALD), or viral hepatitis were included. Liver biopsy was used as the reference standard. Medline and Embase databases were searched. The QUADAS-2 tool was used as a framework to assess risk of bias and applicability. The area under the receiver operator curve (AUROC) was extracted as a summary measure of diagnostic accuracy. RESULTS: Thirty-six studies were included: 11 hepatitis C, 4 hepatitis B, 9 NAFLD, 2 ALD, and 10 mixed. The ELF test showed good diagnostic performance in detecting advanced fibrosis in patients with viral hepatitis (AUROC 0.69 to 0.98) and excellent performance in NAFLD (AUROC 0.78 to 0.97) and ALD (AUROC from 0.92 to 0.94). There is also evidence of good diagnostic performance for detecting cirrhosis in patients with viral hepatitis (AUROC 0.63 to 0.99), good performance in NAFLD (AUROC 0.85 to 0.92), and excellent performance in patients with ALD (AUROC 0.93 to 0.94). CONCLUSION: This systematic review supports the use of the ELF test across a range of CLD as a possible alternative to liver biopsy in selected cases.
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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 | 0.014 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.011 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".