Simplified care-pathway selection for nonspecialist practice: the GLOBAL Primary Biliary Cholangitis Study Group Age, Bilirubin, Alkaline phosphatase risk assessment tool
Bibliographic record
Abstract
Background Opportunity to redefine the care journeys for those living with primary biliary cholangitis (PBC) includes facilitating access to enhanced (PBC-dedicated) programmes by nonspecialist risk ‘flagging’ of patients. Objective To develop a nonexpert PBC stratification tool to help care pathway choices (standard vs. enhanced) choices in PBC. Methods We included ursodeoxycholic acid-treated patients with PBC from the Global PBC Study Group. The performance of baseline and 1-year clinical markers with transplant-free survival was assessed to develop the ‘ABA’ tool using Age (A), Bilirubin (B), and Alkaline phosphatase (A). Added value of fibrosis estimation was assessed. Results ‘ABA’ classification mapped three risk groups (n = 2226): low [Age > 50 years, bilirubin ≤ 1 × ULN, alkaline phosphatase (ALP) ≤ 3 × ULN], high (Age ≤ 50 years, bilirubin > 1 × ULN, ALP > 3 × ULN), and intermediate (other). Transplant-free survival at 10 years in the low-, intermediate-, and high-risk groups were 89, 77, and 59% at baseline and 86, 76, and 40% at 1 year, respectively. We propose that high-risk patients at baseline be directly triaged to enhanced (PBC-dedicated) care and the remaining be reassessed at 1 year. Modelling showed after 1 year 46% patients were proposed to enhanced care and 54% to standard care. The ‘ABA’ mapped pathways facilitated identification of patients at risk based on a young age, as compared to traditional liver biochemical stratification. In patients proposed to standard care, estimated fibrosis stage had ongoing prognostic value. Conclusion Nonspecialist use of the ‘ABA’ risk tool could prioritize care journey choices for patients with PBC.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".