FIB-4-based Referral Pathways Have Suboptimal Accuracy to Identify Increased Liver Stiffness and Incident Advanced Liver Disease
Bibliographic record
Abstract
BACKGROUND & AIMS: Fibrosis-4 (FIB-4) is the cornerstone of identifying clinically relevant liver disease among low-prevalence populations. However, its diagnostic accuracy is debated. METHODS: Participants with metabolic dysfunction from the National Health and Nutrition Examination Survey (NHANES) 2017 to 2020 and Apolipoprotein MOrtality RISk (AMORIS) were used for cross-sectional and longitudinal analysis, respectively. The ability of the FIB-4-based referral pathways to detect individuals with increased liver stiffness (LSM) and/or International Classification of Diseases-based incident advanced liver disease was investigated. Additional analysis included the application of an age-adjusted cutoff. RESULTS: Cross-sectional analysis comprised 6375 participants (age, 52 years [interquartile range, 36-64 years]; 49% male), of whom 10.3% had LSM ≥8, 3.4% LSM ≥12 and 28% FIB-4 ≥1.3. Among those considered to have no clinically relevant liver disease (72%), LSM ≥8, 12, and 15 kPa was still present in 8.5%, 2.3%, and 1.1%, respectively. The FIB-4 had 0.0% sensitivity for increased LSM among participants aged 18 to 35 years and a high referral rate (71%) among participants aged 65 to 80 years or low sensitivity (34%) with the age-adjusted cutoff. However, in the longitudinal analysis (n = 53,766; age, 54 years [interquartile range, 45-61]; male 65%; events 132), FIB-4 detected 74% of 5-year incident cases of advanced liver disease (cirrhosis or hepatocellular carcinoma). CONCLUSIONS: FIB-4-based referral pathways to identify liver disease among the general population result in a high referral rate while not detecting approximately 60% of LSM ≥8, 50% of LSM ≥12, 40% of LSM ≥15, and 25% of incident advanced liver disease. Current referral pathways to detect precirrhotic liver disease in low-prevalence populations could benefit from further optimization.
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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 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".