Comparison of the magnetic resonance elastography and FIB-4 (MEFIB) Index and vibration-controlled transient elastography for significant metabolic dysfunction–associated steatotic liver disease fibrosis
Bibliographic record
Abstract
Background: Significant fibrosis (≥stage 2) in patients with metabolic dysfunction–associated steatotic liver disease (MASLD) is considered a high risk for morbidity and mortality. The magnetic resonance elastography (MRE) and FIB-4 (MEFIB) Index (MRE ≥ 3.3 kPa and FIB-4 ≥ 1.6) has been proposed as an alternative to liver biopsy, particularly in identifying patients for therapeutic intervention. However, MRE is not widely available. Our aim was to compare the MEFIB Index with other simpler, non-invasive markers. Methods: A single-centre retrospective analysis of steatotic liver disease patients with MRE and vibration-controlled transient elastography (VCTE) was carried out between March 2019 and June 2022. Demographic and laboratory data were collected to calculate various fibrosis scores. Results: Our cohort included 77 patients with a mean ± SD age of 51 ± 13 years, 44/77 (57%) female, BMI 34.5 ± 6.7 kg/m 2 , and 33/77 (43%) with diabetes mellitus. Significant MEFIB Index fibrosis (F2–4) compared with F0–1 was significantly associated with older age (61.6 versus 48.9 years), higher VCTE score (18.2 versus 10.6 kPa), NAFLD Fibrosis Score (0.11 versus -1.68), and Aspartate Aminotransferase-To-Platelet Ratio Index (APRI; 1.43 versus 0.44). A logistic regression model showed that age (odds ratio [OR]: 1.16; 95% CI: 1.05–1.29; p = 0.005) and APRI (OR: 10.86; 95% CI: 1.56–75.68; p = 0.016) were independently associated with MEFIB Index and predicted MEFIB F ≥ 2 with an area under the receiver operating characteristic curve of 0.95 (95% CI: 0.87–0.98). Conclusions: In patients with MASLD, simple clinical and biochemical parameters may provide an alternative to predict significant fibrosis based on the MEFIB score. This may be useful in non-tertiary centres where VCTE and MRE are not routinely available.
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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".