What's new in pediatric genetic cholestatic liver disease: advances in etiology, diagnostics and therapeutic approaches
Bibliographic record
Abstract
PURPOSE OF REVIEW: To highlight recent advances in pediatric cholestatic liver disease, including promising novel prognostic markers and new therapies. FINDINGS: Additional genetic variants associated with the progressive familial intrahepatic cholestasis (PFIC) phenotype and new genetic cholangiopathies, with an emerging role of ciliopathy genes, are increasingly being identified. Genotype severity predicts outcomes in bile salt export pump (BSEP) deficiency, and post-biliary diversion serum bile acid levels significantly affect native liver survival in BSEP and progressive familial intrahepatic cholestasis type 1 (FIC1 deficiency) patients. Heterozygous variants in the MDR3 gene have been associated with various cholestatic liver disease phenotypes in adults. Ileal bile acid transporter (IBAT) inhibitors, approved for pruritus in PFIC and Alagille Syndrome (ALGS), have been associated with improved long-term quality of life and event-free survival. SUMMARY: Next-generation sequencing (NGS) technologies have revolutionized diagnostic approaches, while discovery of new intracellular signaling pathways show promise in identifying therapeutic targets and personalized strategies. Bile acids may play a significant role in hepatic damage progression, suggesting their monitoring could guide cholestatic liver disease management. IBAT inhibitors should be incorporated early into routine management algorithms for pruritus. Data are emerging as to whether IBAT inhibitors are impacting disease biology and modifying the natural history of the cholestasis.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".