Serum ferritin levels do not predict the stage of underlying non-alcoholic fatty liver disease.
Bibliographic record
Abstract
BACKGROUND AND AIM: Hepatic endothelial cells release ferritin in response to increased oxidative stress and lipid peroxidation. The principal aim of this study was to determine if serum ferritin levels predict the underlying stage of non-alcoholic fatty liver disease (NAFLD). METHODS: The clinical, biochemical, radiologic and histologic findings of consecutive adult NAFLD patients accessed at a tertiary care center over a 15-year period were analyzed. Those with concurrent liver diseases were excluded. Patients were stratified into three groups based on their histologic stage of disease: simple steatosis, non-alcoholic steatohepatitis (NASH) or cirrhosis. Analysis of Variance and Tukey-Kramer Multiple Comparison tests were used to assess the relationship between serum ferritin levels and stages of NAFLD. RESULTS: 482 patients fulfilled inclusion criteria, including 60 subjects with biopsy proven simple steatosis, 28 subjects with steatohepatitis (NASH) and 20 subjects with histologic or radiologic evidence of cirrhosis. Mean serum ferritin levels were similar in all three groups (simple steatosis: 223.9 ug/L; NASH: 240.7 ug/L; cirrhosis: 271.3 ug/L; p=0.84). NAFLD-induced cirrhotic patients were significantly older, more often diabetic and hypertensive, and had more frequent evidence of splenomegaly and hepatic dysfunction. Following univariate and multivariate modeling, only AST/ALT ratio, diabetes, splenomegaly and age accurately predicted the stage of underlying NAFLD-induced liver disease. CONCLUSION: Hyperferritinemia is common in patients with NAFLD but the extent of serum ferritin elevations do not predict the stage of underlying NAFLD disease.
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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 | 0.000 | 0.002 |
| 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) | 0.001 | 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".