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Record W4282043132 · doi:10.1186/s13223-022-00696-w

Patient selection for milk and egg ladders using a food ladder safety checklist

2022· letter· en· W4282043132 on OpenAlexaffvenue
Gilbert T. Chua, Edmond S. Chan, Joanne Yeung, Scott B. Cameron, Lianne Soller, Brock A. Williams, Alanna Chomyn, Timothy K. Vander Leek, Elissa M. Abrams, Raymond Mak, Tiffany Wong

Bibliographic record

VenueAllergy Asthma and Clinical Immunology · 2022
Typeletter
Languageen
FieldMedicine
TopicFood Allergy and Anaphylaxis Research
Canadian institutionsUniversity of ManitobaUniversity of AlbertaBC Children's HospitalUniversity of British Columbia
FundersUniversity of Hong Kong
KeywordsChecklistFood allergyFood allergensFood safetyAsthmaSelection (genetic algorithm)AnaphylaxisMedicineFood scienceEnvironmental healthAllergyBiologyImmunologyComputer science

Abstract

fetched live from OpenAlex

A food ladder is a form of home-based dietary advancement therapy that gradually increases exposure to an allergenic food through the gradual introduction of egg or milk containing food with increasing quantity and allergenicity from extensively heated forms, such as baked goods, to less processed products. While widely considered safe, the food ladder is not risk-free and most of the egg and milk ladder studies only included preschoolers with mild egg and milk allergies, and with no or well-controlled asthma. We propose a Food Ladder Safety Checklist to assist with patient selection using "4 A's" based on available evidence for food ladders, including Age, active or poorly controlled Asthma, history of Anaphylaxis, and Adherence.

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)0.011
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0020.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0070.004
Insufficient payload (model declined to judge)0.0090.003

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 head0.037
GPT teacher head0.331
Teacher spread0.294 · 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

Citations29
Published2022
Admission routes2
Has abstractyes

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