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Record W4412805777 · doi:10.1016/j.jacig.2025.100547

Egg desensitization is achieved effectively and safely through a low maintenance dose protocol

2025· article· en· W4412805777 on OpenAlexafffundabout
Diana Toscano Rivero, Nofar Kimchi, Wei Zhao, Jana Abi-Rafeh, Danbing Ke, Duncan Lejtenyi, Liane Beaudette, Christine McCusker, Bruce Mazer, Moshe Ben‐Shoshan

Bibliographic record

VenueJournal of Allergy and Clinical Immunology Global · 2025
Typearticle
Languageen
FieldMedicine
TopicFood Allergy and Anaphylaxis Research
Canadian institutionsMontreal Children's HospitalMcGill University Health Centre
FundersFondation de l'Hôpital de Montréal pour enfantsMcGill University Health Centre
KeywordsDesensitization (medicine)Protocol (science)MedicineComputer scienceInternal medicine

Abstract

fetched live from OpenAlex

Background: Egg allergy is a common IgE-mediated food allergy in children. Oral immunotherapy (OIT) reduces allergic reactions via gradual allergen exposure. Although high maintenance doses (1-6 g egg protein) are often used, they carry higher risks of adverse reactions. Evidence on the safety and effectiveness of lower-dose egg OIT (E-OIT) remains limited. Objective: We sought to determine whether a low dose of 300 mg E-OIT is safe and effective for desensitization. Methods: Twenty-two participants were recruited from the Montreal Children's Hospital; 20 were randomized to an immediate-treatment group or an observation group (egg avoidance for 1 year before OIT). Cumulative tolerated dose (CTD), Gal d 1- and Gal d 2-specific IgE (sIgE) and -specific IgG4 (sIgG4), and skin prick test responses were measured at baseline, postescalation, and maintenance. Adverse events were recorded throughout the study. Results: < .001), and sIgG4/sIgE ratios improved. No significant clinical or immunologic changes occurred during the observation period. Conclusions: Targeting a low maintenance dose of 300 mg E-OIT produces significant clinical and immunologic changes in individuals with egg allergy while maintaining low risk of adverse reactions.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.758
Threshold uncertainty score0.474

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.022
GPT teacher head0.388
Teacher spread0.366 · 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.

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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