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Record W3004352153 · doi:10.1097/nt.0000000000000389

Recent Surveys on Food Allergy Prevalence

2020· article· en· W3004352153 on OpenAlexaboutno aff
Mark Messina, Carina Venter

Bibliographic record

VenueNutrition Today · 2020
Typearticle
Languageen
FieldMedicine
TopicFood Allergy and Anaphylaxis Research
Canadian institutionsnot available
Fundersnot available
KeywordsFood allergyEnvironmental healthMedicineAllergenFood allergensHarmAllergyImmunology

Abstract

fetched live from OpenAlex

Substantial numbers of children and adults report having immunoglobulin E–mediated food allergies. However, generating accurate food allergy prevalence data is difficult. Self-reported data can overestimate prevalence when compared with prevalence estimates established by more rigorous methods. As of 2004, in the United States, the Food Allergen Labeling and Consumer Protection Act mandated that the label should declare the source of the food if the product contains that food or a protein-containing ingredient from that food (not all proteins in a major food allergen cause allergic reactions) in the manner described by the law. The 8 major food allergens are milk, eggs, fish, crustacean shellfish, tree nuts, peanuts, wheat, and soybeans, commonly referred to as the “Big 8.” These 8 allergens are thought to account for 90% of the food allergy reactions. Recently published large surveys of Americans and Canadian adults and children provide considerable insight into the prevalence of allergy for the major allergens. These data indicate that there is a large variation in prevalence among the Big 8. The prevalence of soy beans allergy is lower than the prevalence reported for each of the other 7 major allergens, which has been used to argue that soy could be removed from the Big 8 without risking harm to the public. However, the momentum appears to be in favor of expanding the Big 8. The US Food and Drug Administration is evaluating classification of sesame seed as a major allergen; it is already classified as a major allergen in Canada, Australia, and Europe. Europe classifies 14 foods as major allergens. There may be some advantage to standardizing major allergen lists globally, although it may be equally important to acknowledge differences in priority allergens based on cultural and dietary preferences. It is incumbent upon health professionals to help their patients and clients identify foods to which they are allergic and aid in planning diets that are nutritionally adequate despite elimination of these foods.

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)0.002
metaresearch head score (Gemma)0.007
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: none
Teacher disagreement score0.018
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0060.007
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.003

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 head0.061
GPT teacher head0.304
Teacher spread0.243 · 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

Citations98
Published2020
Admission routes1
Has abstractyes

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