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Record W4214541616 · doi:10.1002/hep.32437

Cholestatic liver diseases of genetic etiology: Advances and controversies

2022· review· en· W4214541616 on OpenAlexaff
Samar H. Ibrahim, Binita M. Kamath, Kathleen M. Loomes, Saul J. Karpen

Bibliographic record

VenueHepatology · 2022
Typereview
Languageen
FieldMedicine
TopicPediatric Hepatobiliary Diseases and Treatments
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsEtiologyMedicineGastroenterologyInternal medicine

Abstract

fetched live from OpenAlex

With the application of modern investigative technologies, cholestatic liver diseases of genetic etiology are increasingly identified as the root cause of previously designated "idiopathic" adult and pediatric liver diseases. Here, we review advances in the field enhanced by a deeper understanding of the phenotypes associated with specific gene defects that lead to cholestatic liver diseases. There are evolving areas for clinicians in the current era specifically regarding the role for biopsy and opportunities for a "sequencing first" approach. Risk stratification based on the severity of the genetic defect holds promise to guide the decision to pursue primary liver transplantation versus medical therapy or nontransplant surgery, as well as early screening for HCC. In the present era, the expanding toolbox of recently approved therapies for hepatologists has real potential to help many of our patients with genetic causes of cholestasis. In addition, there are promising agents under study in the pipeline. Relevant to the current era, there are still gaps in knowledge of causation and pathogenesis and lack of fully accepted biomarkers of disease progression and pruritus. We discuss strategies to overcome the challenges of genotype-phenotype correlation and draw attention to the extrahepatic manifestations of these diseases. Finally, with attention to identifying causes and treatments of genetic cholestatic disorders, we anticipate a vibrant future of this dynamic field which builds upon current and future therapies, real-world evaluations of individual and combined therapeutics, and the potential incorporation of effective gene editing and gene additive technologies.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0000.001
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.033
GPT teacher head0.330
Teacher spread0.297 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations63
Published2022
Admission routes1
Has abstractyes

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