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Record W4417060432 · doi:10.3138/canlivj-2025-0019

Fulminant liver failure due to Epstein-Barr virus in immunodeficiency disorders

2025· article· en· W4417060432 on OpenAlexaffvenue
Diana Coman, Julian Hercun, Daniel Corsilli, Hugo Chapdelaine, Guilhem Cros, Marc Bilodeau

Bibliographic record

VenueCanadian Liver Journal · 2025
Typearticle
Languageen
FieldMedicine
TopicViral-associated cancers and disorders
Canadian institutionsMontreal Clinical Research InstituteCentre Hospitalier de l’Université de Montréal
Fundersnot available
KeywordsLiver failureFulminantHuman immunodeficiency virus (HIV)VirusFulminant hepatic failureImmunodeficiencyImmunopathology

Abstract

fetched live from OpenAlex

Background: The majority of patients with Epstein-Barr virus (EBV)-associated hepatitis have a mild clinical course; most infections resolve spontaneously. However, in rare cases, or in patients with underlying immune deficits, outcomes can be fatal. Methods: We used a single-centre case series. Results: In this report, we describe two cases of acute EBV hepatitis. The first patient developed fulminant hepatitis with multi-organ failure and severe immune dysregulation; he died despite maximal intensive-care management. Post-mortem, his bone marrow biopsy results revealed an X-linked lymphoproliferative disease. This primary immunodeficiency impairs the body's ability to control EBV infection and is a risk factor for developing an EBV-positive T-cell lymphoma in childhood. The second patient presented with hepatitis, cytopenias, and hepatosplenomegaly in the context of persistent EBV viremia and responded well to treatment (dexamethasone, rituximab, and supportive care). Immunodeficiency testing was negative in this case. Conclusions: Prompt multidisciplinary management is recommended in cases of severe EBV-associated liver injury.

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: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.006
GPT teacher head0.226
Teacher spread0.220 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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