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Record W4409252005 · doi:10.37349/emed.2025.1001302

Vanishing bile duct syndrome in drug-induced liver injury: clinical and pathologic perspectives

2025· article· en· W4409252005 on OpenAlexaff
Sugunah Sallapan, Kyrillos Faragalla, Hanlin L. Wang, Cherry Pun, Jimin Liu

Bibliographic record

VenueExploration of Medicine · 2025
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicDrug-Induced Hepatotoxicity and Protection
Canadian institutionsUniversity of TorontoWestern UniversityMount Sinai Hospital
Fundersnot available
KeywordsBile ductMedicineLiver injuryDrugGastroenterologyInternal medicinePathologyPharmacology

Abstract

fetched live from OpenAlex

Vanishing bile duct syndrome (VBDS) is a rare condition, representing approximately 0.5% of small bile duct diseases, characterized by progressive destruction of intrahepatic bile ducts, leading to ductopenia. This condition encompasses various etiologies, with drug-induced VBDS (D-VBDS) accounting for 7% of VBDS cases. D-VBDS arises from liver injury due to chemical drugs, traditional medicines, and dietary supplements, often resulting in inflammatory responses and necrosis of bile duct epithelium. Recent years have seen a rise in reported cases, making drug-related injuries a leading cause of acute liver failure in Western countries. This review provides a comprehensive analysis of VBDS, focusing on the histopathological features of acute and chronic D-VBDS, alongside exploring its clinical presentation, prognostic implications, and future research directions. Understanding the diverse etiologies, clinical manifestations, and biochemical parameters associated with D-VBDS is essential for improving diagnosis, treatment strategies, and patient outcomes.

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: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.006

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 studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.244
GPT teacher head0.490
Teacher spread0.246 · 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 designCase report
Domainnot available
GenreEmpirical

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

Citations5
Published2025
Admission routes1
Has abstractyes

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