A Quantitative Assessment of Gluten Cross‐contact in the School Environment for Children With Celiac Disease
Bibliographic record
Abstract
OBJECTIVES: A gluten-free (GF) diet is the primary treatment for celiac disease (CD). Gluten is used in schools, particularly in early childhood, art, and home-economics classrooms. This study aimed to measure gluten transfer from school supplies to GF foods that a child with CD may eat. Also, to measure efficacy of washing techniques to remove gluten from hands and tables. METHODS: Five experiments measured potential gluten cross-contact in classrooms: Play-Doh (n = 30); baking project (n = 30); paper mâché (n = 10); dry pasta in sensory table (n = 10); cooked pasta in sensory table (n = 10). Thirty participants ages 2 to 18 were enrolled. Following activities, gluten levels were measured on separate slices of GF bread rubbed on participant's hands and table surfaces. Participants were assigned 1 of 3 handwashing methods (soap and water, water alone, or wet wipe). Repeat gluten transfer measurements were taken from hands and tables. Gluten measurements made using R-Biopharm R7001 R5-ELISA Sandwich assay. RESULTS: Paper mâché, cooked pasta in sensory tables, and baking project resulted in rates of gluten transfer far greater than the 20 ppm threshold set by Codex Alimentarius Commission. Play-Doh and dry pasta, however, resulted in few gluten transfers to GF bread >20 ppm. Soap and water was consistently the most effective method for removing gluten, although other methods proved as effective in certain scenarios. CONCLUSIONS: The potential for gluten exposure at school is high for some materials and low for others. For high-risk materials, schools should provide GF supplies and have a robust strategy to prevent gluten cross-contact with food.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".