Comparison of diagnostic accuracy and utility of non-invasive tests for clinically significant liver disease in a general population with metabolic dysfunction
Bibliographic record
Abstract
BACKGROUND AND AIMS: Screening for liver disease in the general population requires accurate non-invasive tests (NITs). A head-to-head comparison of NITs for early detection of clinically relevant liver disease among the target population for screening is lacking. APROACH AND RESULTS: Among the meta-cohort (Rotterdam Study and National Health and Nutrition Examination Survey) with metabolic dysfunction aged 18-80 years, 10 NITs were investigated. The diagnostic accuracy for clinically relevant conditions [increased liver stiffness measurement (LSM), at-risk metabolic dysfunction-associated steatohepatitis, advanced fibrosis, or cirrhosis) was assessed. Subgroup analysis included stratification by age group and diabetes/obesity status.We analysed 11,404 participants. Metabolic dysfunction-associated fibrosis 5 (MAF-5) obtained the highest AUC for increased LSM (≥8 kPa: 0.80; ≥12 kPa: 0.87) and advanced fibrosis (AUC: 0.90). Fibrotic NASH index and MAF-5 performed best for detecting metabolic dysfunction-associated steatohepatitis (AUC: 0.93 and AUC: 0.92, p =ns) and SAFE for cirrhosis (AUC: 0.92). To obtain 80% sensitivity for LSM ≥8 kPa, the corresponding MAF-5 cut-off resulted in fewer referrals (42%) compared to fibrosis-4 index (77%) and higher specificity (62% vs. 24%); MAF-5 was also superior for detection of LSM ≥12 kPa and advanced fibrosis. Age-dependent scores yielded lower sensitivity among younger individuals, for example, by referring 20% of the population with the highest NIT scores, the fibrosis-4 index, steatosis-associated fibrosis estimator, NAFLD fibrosis score, FORNS, and Hepamet fibrosis score yielded <10% sensitivity for LSM ≥8 kPa among individuals aged 18-35 years, while fibrotic NASH index and MAF-5 obtained 40% and 71%. CONCLUSIONS: Of the 10 investigated NITs, MAF-5 discriminated best between all conditions except cirrhosis, for which the steatosis-associated fibrosis estimator yielded the highest accuracy. The performance of the fibrosis-4 index was poor, implying that referral pathways for significant liver disease in low-prevalence populations can be improved when more accurate NITs such as MAF-5 are employed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".