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 Of the 115 participating children, the majority (64.6%) were boys. Nearly all of the children (109 of 115 [94.8%]) had peanut allergy, whereas soy and emerging legume allergies were reported by 15.7% and 13.0% of the children, respectively. Of these 115 children, 85 had mono-peanut allergy, 6 had mono-soy allergy, none had emerging legume allergy in the absence of peanut or soy, 12 had peanut and emerging legume allergy, 9 had peanut and soy allergy, and 3 had peanut, soy, and emerging legume allergy. Compared with children aged 0 to 5 years, children aged 6 years or older were significantly less likely to have peanut plus soy or emerging legume allergy (odds ratio = 0.22 [95% CI = 0.05-0.94]; P = .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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 source (direct Gemma or distilled Codex), 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".