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Record W3109010169 · doi:10.1016/j.jaip.2020.11.002

A Consensus Approach to the Primary Prevention of Food Allergy Through Nutrition: Guidance from the American Academy of Allergy, Asthma, and Immunology; American College of Allergy, Asthma, and Immunology; and the Canadian Society for Allergy and Clinical Immunology

2020· article· en· W3109010169 on OpenAlexafffundabout
David M. Fleischer, Edmond S. Chan, Carina Venter, Jonathan M. Spergel, Elissa M. Abrams, David R. Stukus, Marion Groetch, Marcus Shaker, Matthew Greenhawt

Bibliographic record

VenueThe Journal of Allergy and Clinical Immunology In Practice · 2020
Typearticle
Languageen
FieldMedicine
TopicFood Allergy and Anaphylaxis Research
Canadian institutionsUniversity of ManitobaBC Children's HospitalUniversity of British Columbia
FundersSanofi GenzymeGenentechAgency for Healthcare Research and QualityLouisiana Board of RegentsEuropean Academy of Allergy and Clinical ImmunologyNational Peanut BoardNational Institute of Allergy and Infectious DiseasesDanone Nutricia ResearchAsthma and Allergy Foundation of AmericaBausch HealthAimmune TherapeuticsReckitt Benckiser PharmaceuticalsPfizerNestlé Nutrition InstituteFoundation of the American College of Allergy, Asthma & ImmunologySociété Canadienne de PédiatrieAmerican Partnership for Eosinophilic DisordersNorth Carolina GlaxoSmithKline FoundationAstraZenecaAllergy TherapeuticsMead Johnson NutritionFood Allergy Research and EducationNational Institutes of HealthRegeneron PharmaceuticalsAbbott LaboratoriesFood Allergy and Anaphylaxis NetworkAustralasian Society of Clinical Immunology and AllergyAmerican Academy of Allergy Asthma and Immunology
KeywordsMedicineAllergyAsthmaFood allergyImmunology

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.121
metaresearch head score (Gemma)0.127
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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.960
Threshold uncertainty score0.641

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1210.127
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0060.009
Bibliometrics0.0070.005
Science and technology studies0.0060.006
Scholarly communication0.0080.006
Open science0.0130.013
Research integrity0.0300.045
Insufficient payload (model declined to judge)0.0080.006

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 head0.052
GPT teacher head0.370
Teacher spread0.318 · 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
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

Citations299
Published2020
Admission routes3
Has abstractno

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