Validation of FIB-4 for the diagnosis of liver cirrhosis in metabolic dysfunction-associated steatotic liver disease
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".