Nutritional considerations of a paediatric gluten-free food guide for coeliac disease
Bibliographic record
Abstract
The gluten-free (GF) diet is the only treatment for coeliac disease (CD). While the GF diet can be nutritious, increased reliance on processed and packaged GF foods can result in higher fat/sugar and lower micronutrient intake in children with CD. Currently, there are no evidence-based nutrition guidelines that address the GF diet. The objective of this cross-sectional study was to describe the methodological considerations in forming a GF food guide for Canadian children and youth (4-18 years) with CD. Food guide development occurred in three phases: (1) evaluation of nutrient intake and dietary patterns of children on the GF diet, (2) pre-guide stakeholder consultations with 151 health care professionals and 383 community end users and (3) development of 1260 GF diet simulations that addressed cultural preferences and food traditions, diet patterns and diet quality. Stakeholder feedback identified nutrient intake and food literacy as important topics for guide content. Except for vitamin D, the diet simulations met 100 % macronutrient and micronutrient requirements for age-sex. The paediatric GF plate model recommends intake of >50 % fruits and vegetables (FV), <25 % grains and 25 % protein foods with a stronger emphasis on plant-based sources. Vitamin D-fortified fluid milk/unsweetened plant-based alternatives and other rich sources are important to optimise vitamin D intake. The GF food guide can help children consume a nutritiously adequate GF diet and inform policy makers regarding the need for nutrition guidelines in paediatric CD.
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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 | 0.020 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".