A Review on Biomarkers for the Evaluation of Autoimmune Cholestatic Liver Diseases and Their Overlap Syndromes
Bibliographic record
Abstract
Autoimmune cholestatic liver disease includes both Primary Biliary Cholangitis (PBC) and Primary Sclerosing Cholangitis (PSC). Both conditions result in impairment of hepatic bile flow ultimately leading to chronic liver injury, liver fibrosis and eventually end stage cirrhosis. Early and accurate diagnosis are important for the risk stratification, follow up and management of these patients. The underlying pathogenesis of these conditions have not been completely resolved and poses a barrier for the development of new diagnostic and prognostics tools. Current research work suggests that the pathogenesis of autoimmune cholestatic liver disease results from environmental, genetic, and a large component of underlying immune dysfunction. While the current available serum biomarkers and imaging modalities showcases progression in precision medicine for the management of autoimmune cholestatic liver disease, development of new biomarkers are still an area of need in this field. In this review, we will discuss the current and emerging biomarkers in patients with PBC, PSC, and a special population that exhibit overlap syndrome with autoimmune hepatitis (AIH). The use of these biomarkers for diagnosis and prognosis of these patients will be reviewed through the lens of the current understanding of the complex immune pathophysiology of these conditions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".