MétaCan
Menu
← Back to cohort
Record W2565593681 · doi:10.22038/apjmt.2016.6879

Relative Risk of Peanut Allergy across the Globe; Where Toxicology Meets Immunology

2016· article· en· W2565593681 on OpenAlexaff
Herman J. Gibb, Brecht Devleesschauwer, P. Michael Bolger, Janine Ezendam, Julie Cliff, Marco J. Zeilmaker, P Verger, John I. Pitt, Janis Baines, G. O. Adegoke, Reza Afshari, Yan Liu, Bas Bokkers, Henk Van Loveren, Marcel Mengelers, Esther Brandon, Arie H. Havelaar, David C. Bellinger, Angela Randall, Mahmoud Mahmoudi

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2016
Typearticle
Languageen
FieldMedicine
TopicFood Allergy and Anaphylaxis Research
Canadian institutionsIntertek (Canada)BC Centre for Disease ControlUniversity of British Columbia
Fundersnot available
KeywordsAllergyPeanut allergyGlobeImmunologyMedicineToxicologyBiologyFood allergy

Abstract

fetched live from OpenAlex

In December 2015, the World Health Organization (WHO) published the first ever report on the estimates of the global burden of foodborne diseases, which included diseases related to chemical exposures in foods such as peanut allergy. In the report, the burden of disease related to peanut allergies was measured for the European, American and West Pacific Regions. The report showed that unlike other food-related chemical exposures, peanut allergies are far more common in the European and American Regions than in the West Pacific Region. In this commentary we tried to inform physicians and public health workers, and to raise awareness about peanut allergies to facilitate future discussions. Although the WHO report on the estimates of the global burden of foodborne diseases indicates a possible geographical difference in global peanut allergy prevalence, further studies need to compare the relative risk of peanut allergies among individuals of different racial backgrounds in one defined population.

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)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.159
GPT teacher head0.540
Teacher spread0.381 · 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 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
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueDOAJ (DOAJ: Directory of Open Access Journals)→Same topicFood Allergy and Anaphylaxis Research→French-language works237,207→