PERFORMANCE OF NON-INVASIVE TESTS (NITS) AND PREDICTORS OF OUTCOMES IN PATIENTS WITH METABOLIC DYSFUNCTION-ASSOCIATED STEATOTIC LIVER DISEASE (MASLD) FROM LATIN AMERICA AND NORTH AMERICA
Bibliographic record
Abstract
MASLD is highly prevalent worldwide. We evaluated performance of NITs and predictors of outcomes in patients with MASLD from Latin America (LA) as compared to North America (NA). The Global-MASLD project enrolled MASLD patients with liver biopsies and NITs (FIB-4, liver stiffness measurement (LSM) by transient elastography). NITs’ performance to predict advanced fibrosis (AF=F3-F4) and outcomes was assessed. A total of 3,904 MASLD patients were included [N=892 from 5 LA countries (Argentina, Brazil, Chile, Cuba, Mexico) and N=3012 from NA (USA/Canada). MASLD patients from LA were older, had lower BMI (obesity 64% vs. 85%), more lean MASLD (5.6% vs. 2.7%), more T2D (49% vs. 38%) (p<0.001) but similar rates of AF (p=0.56). Clinico-demographic predictors of AF included older age and T2D (p<0.05). The NIT accuracy was lower in LA-MASLD than NA-MASLD: AUC (95% CI) of FIB-4 0.75 (0.71-0.79) vs. 0.81 (0.79-0.83), LSM 0.73 (0.67-0.80) vs. 0.78 (0.75-0.81), Agile-3+ 0.76 (0.70-0.82) for both LA and NA. Sensitivity of 80% (low-risk, screening cutoff) was achieved with FIB-4 ≥1.01 in LA vs. FIB-4 ≥1.17 in NA; specificity of 95% (high-risk, diagnostic cutoff) with FIB-4 ≥2.35 vs. FIB-4 ≥2.40. In adjusted (age, sex, T2D) proportional hazards models, fibrosis severity by histology or NITs was associated with adverse outcomes (death, decompensation, HCC) in both groups (adjusted hazard ratios (aHR) >1.0) (Figure). MASLD patients from LA have more T2D but less obesity than NA. Common NITs have lower accuracy in LA-MASLD. Histologic and NIT stage of fibrosis are independent predictors of adverse outcomes in both groups.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 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".