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Record W4416538769 · doi:10.5409/wjcp.v14.i4.106219

Gestational alloimmune liver disease reconsidered: Advocating for a new nomenclature and enhanced diagnosis accuracy

2025· article· en· W4416538769 on OpenAlexaffabout
Najoua El Helali, Hugo Gagnon, Fernando Álvarez

Bibliographic record

VenueWorld Journal of Clinical Pediatrics · 2025
Typearticle
Languageen
FieldMedicine
TopicLiver Diseases and Immunity
Canadian institutionsCentre Hospitalier Universitaire Sainte-Justine
Fundersnot available
KeywordsPregnancyGestationLiver diseaseFetusGestational ageAntibodyDisease

Abstract

fetched live from OpenAlex

Gestational alloimmune liver disease (GALD), previously known as neonatal hemochromatosis, is a rare antenatal immune condition in which maternal antibodies target the fetal liver, leading to a spectrum of liver injury. Although GALD in the leading cause of neonatal liver failure, recent evidence highlights its association with milder phenotypes. A maternal history of miscarriages or stillbirths may be present. GALD is characterized by hepatic and extrahepatic iron overload sparing the reticuloendothelial system. The transferrin saturation coefficient is the most reliable marker of iron overload, and salivary gland biopsy may assist in diagnosis. Early recognition is crucial, as GALD is treatable. Management involves both acute neonatal treatment and preventive strategies for future pregnancies. Recurrence may reach 90% but can be effectively prevented with antenatal intravenous immunoglobulin therapy. We report four cases of GALD managed in gastroenterology unit of the Sainte-Justine center in Montreal, Canada. A literature review was also conducted to explore the etiopathogenesis, diagnosis, treatment options, and outcomes of the GALD. A total of 39 studies published between 2008 and 2024 were identified through PubMed, Google Scholar, and EMBASE using the terms "gestational alloimmune liver disease" and "neonatal hemochromatosis".

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)0.001
metaresearch head score (Gemma)0.013
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.254
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.013
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 integrity0.0000.001
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 head0.063
GPT teacher head0.404
Teacher spread0.341 · 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.

Study designObservational
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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