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Record W4417186621 · doi:10.1111/pai.70261

Genomic analysis of Canadian children with food allergies points to the immunoglobulin heavy chain gene locus

2025· article· en· W4417186621 on OpenAlexafffundabout
Anne‐Marie Madore, Marie‐Ève Lavoie, Anne‐Marie Boucher‐Lafleur, Frédérique Gagnon‐Brassard, Philippe Bégin, Cloé Rochefort‐Beaudoin, Claudia Nuncio‐Naud, Guy Parizeault, Charles M. Morin, Catherine Laprise

Bibliographic record

VenuePediatric Allergy and Immunology · 2025
Typearticle
Languageen
FieldMedicine
TopicFood Allergy and Anaphylaxis Research
Canadian institutionsCentre Hospitalier Universitaire Sainte-JustineCentre Intégré Universitaire de Santé et de Services Sociaux du Saguenay–Lac-Saint-JeanUniversité du Québec à Chicoutimi
FundersCanada Research Chairs
KeywordsFood allergyAllergyCohortOral immunotherapyPopulationCohort studyPeanut allergyImmunoglobulin E

Abstract

fetched live from OpenAlex

Genomic analysis of Canadian children with food allergies points to the immunoglobulin heavy chain gene locusTo the editor, Food allergy is a major public health concern, affecting about 8% of children in Western countries, 40% of whom are polyallergic.1 The standard approach to managing food allergy is complete avoidance of food allergens.However, avoidance is complicated by the broad presence of the most common allergens.Since symptoms vary from mild to life-threatening anaphylactic shock, 2 it is crucial to better understand the biology underlying the development, diversification, and severity of food allergy.To address this need, a pediatric research clinic was established in Saguenay-Lac-Saint-Jean, a region in northeastern Quebec, Canada.The aim of this initiative is to facilitate access to oral immunotherapy, an emerging treatment for food allergy, and to develop the Zéro allergie cohort.3 This cohort was de-

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.069

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0030.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.008
GPT teacher head0.229
Teacher spread0.221 · 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 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 routes3
Has abstractyes

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