Food allergen ladders: A need for standardization
Bibliographic record
Abstract
INTRODUCTION: The process of gradually reintroducing food allergens into an individual's diet is referred to as food allergen "ladders". There remain many questions regarding the foods chosen, structure and composition of the ladder, and medical and safety considerations. The COVID-19 pandemic has propelled us into an era where medicine is increasingly practiced via online platforms, highlighting the need for standardized food allergen ladder approaches for successful and safe introduction of food allergens. METHODS: We performed a search of currently published food allergen ladders and obtained published information and clinical expertise to summarize current knowledge and suggest future standardized approaches for using food allergen ladders. RESULTS: There are currently a limited number of published milk, egg, wheat, and soy ladders. We suggest the following points should be considered when developing food ladders: (1) Food allergen: dose, time, and temperature of heating of the food allergen, simplicity of the ladder and recipes, the possible role of the wheat matrix, and testing for allergenic protein levels to standardize doses; (2) Nutritional factors: health and nutritional value of the foods in the ladder, taste, texture, and cultural appropriateness of foods should be considered; and (3) Medical aspects: consideration of which patients are safe to undergo ladders outside of the clinical setting, other safety aspects and risk factors for severe reactions, number of days suggested per steps, and availability and provision of rescue medication. Written instructions and recipes should be provided to families who wish to use food allergen ladders. DISCUSSION: Food allergen ladders used for gradual reintroduction of food allergens into a food allergic individual's diet are increasingly being used internationally. Standardization regarding the foods included in the ladder and medical considerations are required to practice patient-centered care, best assist patients and families, and ensure safety.
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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 | 0.166 | 0.184 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.012 | 0.026 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.005 | 0.017 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".