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Record W4409474117 · doi:10.70962/jhi.20250003

Human inborn errors of immunity: 2024 update on the classification from the International Union of Immunological Societies Expert Committee

2025· article· en· W4409474117 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Human Immunity · 2025
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmunodeficiency and Autoimmune Disorders
Canadian institutionsHospital for Sick Children
FundersIntramural Research ProgramJeffrey Modell FoundationFondo Nacional de Desarrollo Científico, Tecnológico y de Innovación TecnológicaNational Institutes of HealthNational Institute of Allergy and Infectious DiseasesNational Health and Medical Research CouncilFonds Wetenschappelijk Onderzoek
KeywordsImmunityMedicinePolitical scienceImmunologyImmune system

Abstract

fetched live from OpenAlex

This report provides an updated classification of inborn errors of immunity (IEIs) involving 508 different genes and 17 phenocopies. Of these, we report 67 novel monogenic defects and 2 phenocopies due to neutralizing anti-cytokine autoantibodies or somatic mutations, which either have been discovered since the previous update (published June 2022) or were reported earlier but have been recently confirmed and/or expanded. The new additions were made after rigorous review of new genetic descriptions of IEIs by the International Union of Immunological Societies (IUIS) Expert Committee using criteria established to define IEI. Although similar pathogenic variants in one gene, in terms of both classes of mutation (missense, nonsense, etc.) and impact on protein function, can result in a spectrum of phenotypic manifestations, they are herein classified according to the most consistently reported phenotype. In addition, because different variants in a single gene can result in recognizable diseases due to gain or loss of function, such cases are classified according to their clinical manifestations as a distinct entry in the same or a different table depending on the associated phenotype. This report will serve as a valuable resource for clinical immunologists and geneticists involved in the molecular diagnosis of individuals with heritable and acquired immunological disorders. Moreover, we expect this report to also serve as a valuable resource for all disciplines of medicine, since patients with IEIs may be first seen by rheumatologists, hematologists, allergists, dermatologists, neurologists, gastroenterologists, and pulmonologists, depending upon their spectrum of presenting clinical features. Finally, expanding the known monogenic and related causes of human immune diseases requires dissection of underlying cellular and molecular mechanisms, which reveals fundamental requirements for specific genes, pathways, processes, and even cell types. Such knowledge may not only contribute to improved patient diagnosis and management but also pave the way to the development and implementation of therapies that target the cause-rather than the symptoms-of these conditions.

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.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.678
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.034
GPT teacher head0.295
Teacher spread0.261 · 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