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Record W4385851122 · doi:10.1007/s12072-023-10578-x

Management of adults with Alagille syndrome

2023· review· en· W4385851122 on OpenAlexaff
Mohammed D. Ayoub, Ahmad A. Bakhsh, Shannon M. Vandriel, Verena Keitel, Binita M. Kamath

Bibliographic record

VenueHepatology International · 2023
Typereview
Languageen
FieldMedicine
TopicPediatric Hepatobiliary Diseases and Treatments
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsAlagille syndromeMedicineLiver transplantationCholestasisHepatologyProgressive familial intrahepatic cholestasisLiver diseaseDiseaseIntensive care medicineTransplantationPediatricsInternal medicine

Abstract

fetched live from OpenAlex

Alagille syndrome (ALGS) is a complex rare genetic disorder that involves multiple organ systems and is historically regarded as a disease of childhood. Since it is inherited in an autosomal dominant manner in 40% of patients, it carries many implications for genetic counselling of patients and screening of family members. In addition, the considerable variable expression and absence of a clear genotype-phenotype correlation, results in a diverse range of clinical manifestations, even in affected individuals within the same family. With recent therapeutic advancements in cholestasis treatment and the improved survival rates with liver transplantation (LT), many patients with ALGS survive into adulthood. Although LT is curative for liver disease secondary to ALGS, complications secondary to extrahepatic involvement remain problematic lifelong. This review is aimed at providing a comprehensive review of ALGS to adult clinicians who will take over the medical care of these patients following transition, with particular focus on certain aspects of the condition that require lifelong surveillance. We also provide a diagnostic framework for adult patients with suspected ALGS and highlight key aspects to consider when determining eligibility for LT in patients with this syndrome.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.954
Threshold uncertainty score0.737

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.035
GPT teacher head0.347
Teacher spread0.312 · 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 teacher head, 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

Citations19
Published2023
Admission routes1
Has abstractyes

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