Baseline Characteristics and Risk Profiles of 1111 Patients With Primary Biliary Cholangitis (PBC) in Need of Second-Line Therapy
Bibliographic record
Abstract
Up to 40% of patients receiving first-line ursodeoxycholic acid (UDCA) for PBC have an ALP≥1.67×ULN and progress. We examined patients who screen failed due to ALP<1.67×ULN in 4 seladelpar clinical trials (2015–2022); 1111 patients with PBC were screened after UDCA treatment≥12 months or UDCA intolerance. ALP≥1.67×ULN was required for enrollment. Baseline characteristics and risk profiles of enrolled patients (EN, ALP≥1.67×ULN) and screen failures (SF, ALP≥ULN, but<1.67×ULN) were compared. We stratified using proportions with Enhanced Liver Fibrosis (ELF) values≥10.0 or bilirubin>0.6×ULN. The relationship of ELF and liver stiffness was confirmed when available. Screened patients were predominantly female (94%) with mean (SD) age of 57±9.5 years. Studies enrolled 54% of screened patients (n=603; EN cohort) with PBC duration 8±6.5 years and UDCA dose 15±3.9 mg/kg/day (92% on UDCA). Overall, 26% of patients (n=284) screen failed due to ALP>ULN but<1.67×ULN (SF cohort). Differences in baseline ALP, GGT, and ALT were observed between cohorts. Higher-risk bilirubin levels were present in 51.1% and 42.0% of EN and SF cohorts, respectively. Elevated risk based on ELF was identified in 43.2% of EN and 27.2% of SF cohorts. Liver stiffness was assessed in 66% of EN patients; mean liver stiffness of 9.7 kPa correlated with ELF (r=0.50, P<0.001). Thus, patients with ALP≥ULN, but<1.67×ULN, often have risk factors for disease progression and should be assessed for second-line therapies. Publication History Article published online: 20 January 2025 © 2025. Thieme. All rights reserved. Georg Thieme Verlag KG Oswald-Hesse-Straße 50, 70469 Stuttgart, Germany
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".