A220 THE IMPACT OF DIABETES AND AGE ON PERFORMANCE OF NON-INVASIVE SERUM-BASED TESTS FOR PREDICTION OF ADVANCED FIBROSIS IN BIOPSY-PROVEN NAFLD
Bibliographic record
Abstract
Abstract Background Non-alcoholic fatty liver disease (NAFLD) is now a leading cause of end-stage liver disease. Advanced stage F3-4 fibrosis predicts liver-related mortality in NAFLD patients. Simple non-invasive serum-based tests (NIT) for F3-4 are limited by indeterminate scores, necessitating secondary tests or liver biopsy. Diagnostic NIT cut-offs may vary in NAFLD patients with diabetes mellitus (DM) and the elderly. Identifying appropriate thresholds in populations in which these tests can be applied will reduce indeterminates and facilitate their broader use. Aims The aim of this study was to assess the impact of DM status and age on the performance of NIT for prediction of advanced fibrosis in patients with biopsy-proven NAFLD. Methods Patients presenting to two Canadian tertiary care centers between 2010–2018 for liver biopsy to diagnose NAFLD were included in this study. NIT including NFS, FIB4, BARD, AST-to-platelet ratio index (APRI), and AST to ALT ratio (AST/ALT) were calculated for each patient using validated cut-offs. Results 457 patients were included in this study. Mean age was 48.8±12.9 years, 56% male, mean BMI 32.3 ± 6.7kg/m2, 69% with DM, and F3-4 prevalence 48%. Indeterminate rates for NIT were generally higher for older patients, with or without DM (27–49% and 37–52%, vs. 33–42% and 20–37%, respectively). FIB-4 and NFS both had high specificity >0.9 in DM patients <60 years (Table 1). There were no differences in AUROC for individual NITs between patients with and without DM, and those < 60 vs. ≥ 60, nor between individual NIT within these groups. Conclusions DM status and age, ≥ 60 vs. < 60, do not appear to have a significant impact on diagnostic performance of serum-based NIT in our cohort. Older patients had higher indeterminate results and reduced specificity, but T2DM status and age did not appear to have an impact on rate of misclassified patients. Serum-based NIT thresholds need to be optimized for older patients to reduce indeterminates and improve specificity. Funding Agencies None
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".