Cytokeratin-18 and hyaluronic acid levels predict liver fibrosis in children with non-alcoholic fatty liver disease.
Bibliographic record
Abstract
OBJECTIVES: There is a need to replace liver biopsy with non-invasive markers that predict the degree of liver fibrosis in fatty liver disease related to obesity. Therefore, we studied four potential serum markers of liver fibrosis and compared them with histopathological findings in liver biopsy in children with non-alcoholic fatty liver disease (NAFLD). METHODS: We determined fasting serum level of hyaluronic acid (HA), laminin, YKL-40 and cytokeratin-18 M30 in 52 children (age range 4-19, mean 12 years, 80 % of them were overweight or obese) with biopsy-verified NAFLD. Viral hepatitis, autoimmune and metabolic liver diseases (Wilson's disease, alpha-1-antitrypsin deficiency, cystic fibrosis) were excluded. Fibrosis stage was assessed in a blinded fashion by one pathologist according to Kleiner. Receiver operating characteristics (ROC) analysis was used to calculate the power of the assays to detect liver fibrosis (AccuROC, Canada). RESULTS: Liver fibrosis was diagnosed in 19 children (37 %). The levels of HA and CK18M30 were significantly higher in children with fibrosis compared to children without fibrosis (p=0.04 and 0.05 respectively). The ability of serum HA (cut-off 19.1 ng/ml, Se=84 %, Sp=55 %, PPV=52 %, NPV=86 %) and CK18M30 (cut-off 210 u/l, Se=79 %, Sp=60 %, PPV=56 %, NPV=82 %) to differentiate children with fibrosis from those without fibrosis was significant (AUC=0.672 and 0.666, respectively). The combination of both markers was superior (AUC=0.73, p=0.002). Laminin and YKL-40 levels did not allow a useful prediction. CONCLUSIONS: Cytokeratin-18 and hyaluronic acid are suitable serum markers predicting liver fibrosis in children with NAFLD. Studying these markers may identify patients at risk of disease progression.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".