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Record W4407394311 · doi:10.1186/s13223-025-00948-5

Food oral immunotherapy

2025· review· en· W4407394311 on OpenAlexaffvenueabout
Mary McHenry, Philippe Bégin, Edmond S. Chan, Meriem Latrous, Harold Kim

Bibliographic record

VenueAllergy Asthma and Clinical Immunology · 2025
Typereview
Languageen
FieldMedicine
TopicFood Allergy and Anaphylaxis Research
Canadian institutionsMcMaster UniversityWestern UniversityChildren's Hospital of Eastern OntarioDalhousie UniversityBC Children's HospitalUniversity of British ColumbiaUniversité de MontréalIzaak Walton Killam Health Centre
Fundersnot available
KeywordsOral immunotherapyMedicineFood allergyToddlerIntervention (counseling)Intensive care medicineOral food challengeFood allergensImmunotherapyPopulationAllergyImmunoglobulin EImmunologyAntibodyEnvironmental healthImmune systemPsychology

Abstract

fetched live from OpenAlex

Food oral immunotherapy (OIT) is an option for the treatment of immunoglobin E (IgE)-mediated food allergy that involves administering gradually increasing doses of an allergenic food over time (under medical supervision) with the goal of desensitizing an individual to the food allergen. Current Canadian clinical practice guidelines for OIT recommend this form of therapy as an option in patients with food allergy. The intervention should be prioritized in the infant and toddler population, in which it is particularly well tolerated and can lead to sustained unresponsiveness (also sometimes referred to as remission). In this article, we provide an overview of OIT and discuss the role non-allergist clinicians can play in caring for patients undergoing OIT.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.015
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0150.008

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 head0.066
GPT teacher head0.421
Teacher spread0.354 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations7
Published2025
Admission routes3
Has abstractyes

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