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 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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".