Milk allergy most burdensome in multi‐food allergic children
Bibliographic record
Abstract
BACKGROUND: Food allergy is a substantial health burden, which disproportionately affects children. Among children with food allergy, as many as 70% have multiple food allergies. Whereas the overall burden of food allergy on quality of life has been described, little is known about the burden of individual allergens. We aimed to examine the perception of burden among families with multiple food-allergic children. METHODS: Parents of children with 1 + children with multiple food allergies including milk responded to online questions, including both open-ended and closed-ended questions on food allergy-related burdens of time, financial costs, social restrictions, and emotional demands. RESULTS: Overall, 64 children (69.8% boys) of whom (73.0%) most were aged 10 and younger were included. Most had been diagnosed with food allergy in infancy and by a (pediatric) allergist. Other common allergies included peanut (65.6%), tree nuts (57.8%), egg (76.6%), and sesame (31.3%). Quantitatively, milk allergy was reported as carrying the most burden, including most socially limiting (81.5%), requiring the most planning (75.9%), causing the most anxiety (68.5%), most challenging to find "safe" or allergy-friendly foods (72.2%), and costly (81.5%). Qualitatively, we identified five themes that captured burdens associated with costs, marketing of milk products to children, risk of cross-contamination, ubiquity of milk/dairy and public confusion with lactose intolerance, and an unwillingness of others to accommodate the allergy. CONCLUSION: Parents whose children have multiple food allergies, including milk, report milk as the allergy associated with the greatest time, financial, social, and emotional burdens.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".