Allergic reactions to emerging food allergens in Canadian children
Bibliographic record
Abstract
Most Canadian food allergy data has focused on Health Canada's priority food allergens. This study describes which non-priority (emerging) food allergens were most commonly reported by Canadian parents and categorized/confirmed by allergists. A secondary aim was to describe severity of allergic reactions to emerging allergens. Parents reported allergic reactions to emerging food allergens experienced by their child (< 18 years) which occurred in the past 12 months, and allergists categorized/confirmed them according to likelihood of IgE-mediated food allergy. Of 68 eligible patients completing the survey, the most commonly reported emerging allergens were fruits/vegetables (58.8%), seeds (22.1%), legumes (19.1%) and other (11.8%). Median allergist ranking for legumes was 'probable' IgE-mediated food allergy, 'possible' for seeds and fruits/vegetables, and 'unlikely' for other. Median reaction severity was mild for legumes, and moderate for seeds, fruits/vegetables, and other. Our study highlights that non-priority food allergens, namely legumes and seeds, can lead to probable/likely allergic reactions in Canadian children. These food allergens are increasing in popularity in the Canadian diet, which could lead to increasing reports of allergic reactions. More research is needed to confirm reports of reactions to emerging allergens, and to document their inclusion as ingredients in packaged foods.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".