Age-dependent differences in FIB-4 predictions of fibrosis in patients with MASLD referred from primary care
Bibliographic record
Abstract
BACKGROUND: Fibrosis 4 (FIB-4) is widely used to triage patients with metabolic dysfunction-associated steatotic liver disease. Given that age is part of FIB-4, higher scores may be expected in the elderly population. This led to the proposal of using a higher threshold of FIB-4 to triage patients aged ≥65. Our main objective is to evaluate how age modifies the association between the FIB-4 index and disease severity based on the vibration-controlled transient elastography (VCTE) "rule of 5s." METHODS: In this cross-sectional study, we prospectively analyzed data from a primary care referral pathway. We used liver stiffness measurement by VCTE as a reference standard for liver risk. We modeled with ordinal regression the exceedance probabilities of finding different liver stiffness measurement thresholds according to FIB-4, and how age modifies FIB-4 predictions. RESULTS: Nine hundred eighty-five participants with complete data were used for modeling. Participants aged ≥65 had a higher prevalence of advanced liver disease estimated by VCTE and higher FIB-4 values than those <65 (85.9% vs. 20.2% for FIB-4 ≥1.3, and 46.5% vs. 6.5% for FIB-4 ≥2.0). In participants age ≥65, the negative predictive value for VCTE ≥10 kPa of FIB-4 <1.3 was 100% versus FIB-4 <2.0 was 83%. Age significantly modified FIB-4-based prediction of fibrosis, but predictions at a threshold of 1.3 or 2 were only minimally altered. For higher FIB-4 threshold (ie, 2.7), age strongly modified FIB-4 predictions of liver stiffness measurement. CONCLUSIONS: Age does not relevantly modify FIB-4 predictions when using the common threshold of 1.3. Our data suggest no rationale for increasing the FIB-4 threshold to 2 for undergoing further testing in patients aged ≥65. However, the meaning of a FIB-4 of 2.7 strongly changes with age. This cutoff for ages over 65 is not enough to define high-risk and would not warrant direct referral.
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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".