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Pediatric Cholestatic Diseases: Common and Unique Pathogenic Mechanisms

2024· review· en· W4391176052 on OpenAlexaff
Harry Sutton, Saul J. Karpen, Binita M. Kamath

Bibliographic record

VenueAnnual Review of Pathology Mechanisms of Disease · 2024
Typereview
Languageen
FieldMedicine
TopicPediatric Hepatobiliary Diseases and Treatments
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
FundersNational Institute of Diabetes and Digestive and Kidney Diseases
KeywordsCholestasisAlagille syndromeEnterohepatic circulationBiliary atresiaPrimary sclerosing cholangitisProgressive familial intrahepatic cholestasisBile acidMedicineGastroenterologyInternal medicinePrimary biliary cirrhosisIntrahepatic bile ductsBile ductDiseaseLiver transplantation

Abstract

fetched live from OpenAlex

Cholestasis is the predominate feature of many pediatric hepatobiliary diseases. The physiologic flow of bile requires multiple complex processes working in concert. Bile acid (BA) synthesis and excretion, the formation and flow of bile, and the enterohepatic reuptake of BAs all function to maintain the circulation of BAs, a key molecule in lipid digestion, metabolic and cellular signaling, and, as discussed in the review, a crucial mediator in the pathogenesis of cholestasis. Disruption of one or several of these steps can result in the accumulation of toxic BAs in bile ducts and hepatocytes leading to inflammation, fibrosis, and, over time, biliary and hepatic cirrhosis. Biliary atresia, progressive familial intrahepatic cholestasis, primary sclerosing cholangitis, and Alagille syndrome are four of the most common pediatric cholestatic conditions. Through understanding the commonalities and differences in these diseases, the important cellular mechanistic underpinnings of cholestasis can be greater appreciated.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.017
GPT teacher head0.341
Teacher spread0.324 · 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

Citations30
Published2024
Admission routes1
Has abstractyes

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