Combined and sequential non-invasive approach to diagnosing non-alcoholic steatohepatitis in patients with non-alcoholic fatty liver disease and persistently normal alanine aminotransferase levels
Bibliographic record
Abstract
BACKGROUND AND AIM: Imaging-confirmed non-alcoholic fatty liver disease (NAFLD) with normal alanine aminotransferase (nALT) levels is infrequently the subject for further evaluation. Early diagnosis of non-alcoholic steatohepatitis (NASH) is needed to prevent disease progression. Thus, we tested the clinical utility of serum Golgi protein 73 (GP73) levels and developed a new non-invasive score to diagnose NASH in patients with biopsy-confirmed NAFLD and persistent nALT levels. METHODS: Serum GP73 and cytokeratin-18 M30 fragments (CK18-M30) levels were measured in 345 patients with biopsy-proven NAFLD. We developed a new score, named G-NASH model (by incorporating serum GP73), and combined it with serum CK18-M30 measurement in a sequential non-invasive approach to accurately identify NASH among patients with NAFLD and persistent nALT levels. RESULTS: 105 (30.4%) patients had persistent nALT, 53 of whom had histologically confirmed NASH. Both serum GP73 and CK18-M30 levels alone had poor diagnostic accuracy in identifying NASH (55.2% and 51.6%, respectively) in these patients. Conversely, G-NASH model performed better than other established non-invasive scoring systems, and by using our proposed sequential non-invasive approach 82.9% of patients with NASH were correctly identified. CONCLUSIONS: NASH is highly prevalent in patients with NAFLD with persistent nALT levels. The G-NASH model accurately identifies NASH in this patient group.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".