Elevated serum ferritin in non-alcoholic fatty liver disease is not predictive of fibrosis
Bibliographic record
Abstract
BACKGROUND: Non-alcoholic fatty liver disease (NAFLD) is common with widely ranging severity. Non-invasive risk scores for risk stratification are recommended but misclassify a significant proportion of patients. In situations where non-invasive risk scores do not provide guidance, referral is typically made to a Hepatologist for transient elastography or liver biopsy. Serum ferritin is elevated in many patients with NAFLD related to dysmetabolic and inflammatory hyperferritinemia. Ferritin is widely available and part of a standard workup for chronic liver disease. METHODS: To explore the association of ferritin and risk of fibrosis in NAFLD, we reviewed patients diagnosed with NAFLD at the hepatology clinic of the Vancouver General Hospital between the years of 2015 and 2018. We collected data on 317 patients retrospectively assessing for a relationship between serum ferritin and elastography score. RESULTS: Two hundred twenty-four patients were included in the final analysis. Median ferritin was 145 µg/L (IQR 62–311). Median liver stiffness was 5.2 kPa with 14.3% of patients having liver stiffness ≥8.7 kPa and 17.4% ≥ 8.0 kPa. ROC curve analysis using a liver stiffness ≥8.0 kPa as a cutoff for F2 fibrosis showed an AUROC of 0.54 for serum ferritin levels. At a cut-off of both 300 µg/L; and 450 µg/L median liver stiffness did not differ significantly in those with ferritin above the cutoff (ferritin ≥300 µg/L; p = 0.099, ferritin ≥450 µg/L; p = 0.12). Ferritin was significantly higher in male patients (198 versus 91 µg/L; p = 0.0001). There was a weak linear association between AST and ferritin levels. CONCLUSION: In this cohort of 224 patients with NAFLD, serum ferritin was not predictive of significant liver fibrosis.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".