Global performance of non-invasive tests in MASLD: Insights from the G-MASLD study
Bibliographic record
Abstract
BACKGROUND: Metabolic dysfunction-associated steatotic liver disease (MASLD) is highly prevalent worldwide. Performance of non-invasive tests (NITs) in patients with MASLD recruited from different regions of the world was evaluated. METHODS: MASLD patients with liver biopsies and NIT data [fibrosis-4 index (FIB-4), enhanced liver fibrosis (ELF), and liver stiffness measurement (LSM)] were enrolled through the Global NASH Council collaboration (G-MASLD). FibroScan-AST (FAST) and Agile-3+/Agile-4 were calculated. NITs' performance for predicting ≥F2 (significant fibrosis), ≥F3 (advanced fibrosis), or cirrhosis (F4) was determined in patients from different regions. RESULTS: A total of 17,792 MASLD patients from 41 countries were included: 14% had F0, 32% F1, 18% F2, 22% F3, 13% F4 (cirrhosis); 48% NAS ≥5. Advanced fibrosis prediction by NITs was variable across regions for FIB-4 [pooled AUC (95% CI)=0.80 (0.79-0.81)], the lowest in Latin America [0.75 (0.71-0.79)], the highest in MENA [0.84 (0.82-0.87)], and ELF [pooled AUC=0.77 (0.76-0.79)], the lowest in Europe [0.72 (0.69-0.76)], the highest in North America [0.80 (0.78-0.82)]. Prediction of advanced fibrosis by LSM [pooled AUC=0.84 (0.83-0.85)] was similar across regions except North America [0.78 (0.76-0.81)]. In addition, FAST [AUC=0.75 (0.74-0.76)] and Agile-3+ [AUC=0.87 (0.86-0.88)] performed similarly across regions. Similar trends were observed for the NITs predicting significant fibrosis. Finally, the accuracy of Agile-4 for predicting cirrhosis [AUC=0.90 (0.89-0.91)] was the lowest in North America [0.85 (0.83-0.87)], the highest in MENA [0.96 (0.94-0.98)]. CONCLUSIONS: The diagnostic performance of common NITs for fibrosis in MASLD varies across the world. In the large multinational G-MASLD sample, the most accurate NITs were Agile-3+ and Agile-4 composite scores.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".