Health-related quality of life worsens by school age amongst children with food allergy
Bibliographic record
Abstract
Food allergy is negatively associated with health-related quality of life (HRQL). Although differences exist between parents and children, less is known about age-specific differences amongst children. As such, we aimed to identify if age, as well as other factors, are associated with food allergy-specific HRQL in an objectively defined population of children. Overall, 63 children (boys: n = 36; 57.1%) with specialist-diagnosed food allergy to 1 + foods were included. Parents/guardians completed the Swedish version of a disease-specific questionnaire designed to assess overall- and domain-specific HRQL. Descriptive statistics and linear regression were used. The most common food allergy was hen’s egg (n = 40/63; 63.5%). Most children had more than one food allergy (n = 48; 76.2%). Nearly all had experienced mild symptoms (e.g. skin; n = 56/63; 94.9%), and more than half had severe symptoms (e.g. respiratory; 39/63; 66.1%). Compared to young children (0–5 years), older children (6–12 years) had worse HRQL (e.g. overall HRQL: B = 0.60; 95% CI 0.05–1.16; p < 0.04.). Similarly, multiple food allergies, and severe symptoms were significantly associated with worse HRQL (all p < 0.05) even in models adjusted for concomitant allergic disease. No associations were found for gender or socioeconomic status. Older children and those with severe food allergy have worse HRQL.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".