Validation of FIB-4 for the diagnosis of liver cirrhosis in metabolic dysfunction-associated steatotic liver disease
Why this work is in the frame
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Bibliographic record
Abstract
American Association for the Study of Liver Diseases practice guidance on metabolic dysfunction-associated steatotic liver disease (MASLD) has recommended using specific cut-off values for the Fibrosis-4 index (FIB-4) to detect cirrhosis. A cut-off of 3.48 is recommended for identifying stage 4 fibrosis (F4) with high specificity, while a cut-off of 1.67 is suggested for ruling out advanced fibrosis. Our study aimed to validate the diagnostic performance of these new FIB-4 cut-offs in our cohort of biopsy-proven MASLD from two Canadian tertiary care centres. Our study included 390 patients with biopsy-proven MASLD with F4 prevalence of 22%. Among the 87 patients with cirrhosis, 37 (42.5%) were correctly identified with a FIB-4 ≥3.48. FIB-4 had an area under the receiver operating characteristic curve of 0.79 at the proposed cut-off points, with 32% of patients being indeterminate or misclassified. Sensitivity and positive-predictive value for the FIB-4 cut-off were 65% and 68.5%, respectively, while the specificity and negative-predictive value were 93% and 92%, respectively. In conclusion, in our biopsy-proven MASLD cohort, recommended FIB-4 cut-offs ≥3.48 and <1.67 had low sensitivity but high specificity. An upper FIB-4 cut-off of 3.48 would have missed nearly one in four cirrhosis cases. The proposed FIB-4 thresholds for identifying F4 in MASLD patients have limited diagnostic utility in higher prevalence tertiary hepatology cohorts.
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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 | 0.001 | 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 it