Peanut, soy, and emerging legume allergy in Canada
Bibliographic record
Abstract
Background: Individuals with 1 legume allergy may be cosensitized to other legumes and thus may potentially have other legume allergies as well. Although the use of emerging legumes (eg, pea, lentils, chickpeas) in commercial food production is increasingly common, the literature has largely focused on peanut and soy, both of which are priority allergens in Canada. Objective: We aimed to describe the distribution of priority and emerging legume allergies in Canada, with consideration for patient age. Methods: Cross-sectional survey data collected between 2019 and 2021 from families who follow food allergy-related social media platforms were queried for demographics, as well as for food allergy (including by type and number of foods and by age [0-5 vs ≥6 years]). Data were described and then analyzed by using logistic regression and adjusted for sex, age at diagnosis, and number of food allergies. Results: = .04). Conclusion: Of the children with peanut allergy, a considerable number also had peanut allergy and soy allergy and/or another legume allergy. Younger children have higher odds of multiple legume allergy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".