MétaCan
Menu
Back to cohort
Record W2735988860 · doi:10.1080/21645515.2017.1339844

Update on oral immunotherapy for egg allergy

2017· review· en· W2735988860 on OpenAlexaff
François Graham, Natacha Tardio, Louis Paradis, Anne Des Roches, Philippe Bégin

Bibliographic record

VenueHuman Vaccines & Immunotherapeutics · 2017
Typereview
Languageen
FieldMedicine
TopicFood Allergy and Anaphylaxis Research
Canadian institutionsMcGill University Health CentreHôpital Notre-DameCentre Hospitalier Universitaire Sainte-Justine
Fundersnot available
KeywordsOral immunotherapyDesensitization (medicine)MedicineEgg allergyAllergyImmunotherapyClinical trialImmunologyRandomized controlled trialFood allergyIntensive care medicineImmune systemInternal medicine

Abstract

fetched live from OpenAlex

Oral immunotherapy (OIT) is an emerging treatment of IgE-mediated egg allergy. In the past decade, a multitude of studies have assessed the potential for egg OIT to induce clinical desensitization. The following review will evaluate the efficacy and safety of this therapy as determined by randomized controlled, non-randomized controlled and uncontrolled trials. Recent studies using reduced allergenic egg products and anti-IgE assisted therapy to improve egg OIT safety will also be discussed. Recent advances in the mechanisms underlying food OIT suggest that certain immune parameters may be helpful in monitoring response to therapy, including egg OIT. Although, egg OIT is consistently shown to be effective with regards to clinical desensitization, fewer studies have looked at persistent tolerance or sustained unresponsiveness. Limited results of long-term follow-up trials suggest that this therapy may have disease-modifying effects. In general, the comparison of studies is complicated by major differences in study designs, OIT protocols and endpoints.

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)0.001
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 score0.011
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0110.004

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 head0.211
GPT teacher head0.462
Teacher spread0.251 · 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

Citations19
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueHuman Vaccines & ImmunotherapeuticsSame topicFood Allergy and Anaphylaxis ResearchFrench-language works237,207