A217 NONINVASIVE ASSESSMENTS TO IDENTIFY PATIENTS WITH ADVANCED FIBROSIS DUE TO NASH: SCREENED POPULATION FROM THE REGENERATE TRIAL
Bibliographic record
Abstract
Abstract Aims We explored the ability of noninvasive tests (NITs) to identify patients (pts) with advanced fibrosis due to NASH. Methods All screened pts from the ongoing phase 3 REGENERATE with available histology data were included. Five NITs were evaluated using established literature cutoffs to identify or exclude advanced fibrosis (values between upper and lower thresholds were considered indeterminate): Aspartate Transaminase-to-Platelet Ratio Index (APRI; ≥0.57, ≤0.84), Enhanced Liver Fibrosis (ELF; ≥7.7, <9.8), Fibrosis-4 (FIB-4; ≥1.30, <2.67), NAFLD fibrosis score (NFS; ≥−1.455, <0.676), and Transient Elastography (TE; ≥7.9 kPa, <9.6 kPa). Three testing methods applied were single NIT, 2 simultaneous NITs weighted equally (NFS+ELF, FIB-4+ELF, NFS+TE, FIB-4+TE), and 2 sequential NITs with the second test performed only if the first test was indeterminate (NFS→ELF, FIB-4→ELF, NFS→TE, FIB-4→TE). Results 4133 pts in the REGENERATE screened population had an available biopsy (baseline liver biopsy: F0, 15.5%; F1, 27.2%; F2, 21.2%; F3, 29.6%; F4, 6.5%). Of these, 96% had FIB-4, NFS, and APRI, 41% had TE, and 28% had ELF. Single NITs with upper thresholds demonstrating strong specificity for identification of advanced fibrosis were FIB-4 (97%), NFS (94%), and APRI (86%); NITs with lower thresholds demonstrating good sensitivity for identification of early fibrosis were ELF (100%) and TE (88%). Evaluation of 2 simultaneous NITs resulted in a greater percentage of pts in the indeterminate zone. Application of 2 sequential tests improved the accuracy of identification and reduced misclassification vs 2 simultaneous tests. Conclusions Sequential NIT strategies may decrease liver biopsy rates while maintaining the accuracy of noninvasive diagnosis in pts with advanced fibrosis due to NASH. Funding Agencies Intercept Pharmaceuticals
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".