Veganism and paediatric food allergy: two increasingly prevalent dietary issues that are challenging when co-occurring
Bibliographic record
Abstract
Vegan diets - defined as the exclusion of all foods of animal origin from the diet- are becoming popular. In recent years, the prevalence of food allergy has also increased, and disproportionately affects children. When vegan diets and food allergy co-occur, this combination can be challenging and pose risks of nutritional deficiencies, particularly during childhood. In this paper, we aim to summarise the major concerns regarding vegan diets and food allergy, review the literature on this topic, and provide some suggestions for healthcare providers, particularly dietitians and nutritionists, who work with food allergic, vegan patients and their family. When working with this patient population, a regular and complete medical nutrition history, including screening for any possible nutritional deficiencies, is warranted. Likewise, the routine tracking of serum markers (especially iodine, iron, zinc, calcium, Vitamins B12, D, B2, and A, selected n-3 fatty acids and protein, which are more abundant in animal vs. plant foods) and symptoms of co-morbid diseases, including asthma, is important, as comorbid diseases may increase energy and nutrient requirements. For infants and children, anthropometry ought to be tracked longitudinally at regular intervals to identify any deviations from the child's previous growth pattern, and to accommodate any increased requirements for growth and development. Correct diagnoses, education and allergy management must be disseminated to the family in a clear and appropriate manner. Children with allergy may have increased nutritional needs due to comorbidity. This is complicated by coincident food allergy and vegan diet as both impose diet restrictions (limiting sources of important nutrients, need for dietary variety and/or increased consumption due to reduced bioavaliability).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".