Grain Products Are a Top Source of Energy and Nutrients among Nova Scotian Adults Following a Gluten-Free Diet
Bibliographic record
Abstract
To determine the food sources of energy and 13 core nutrients, 89 diet recalls were analyzed from an explanatory mixed-methods pilot study with adults following a gluten-free diet (GFD) for any reason. Nonconsecutive dietary recalls were collected through a web-based, Automated Self-Administered 24-Hour (ASA24®—Canada-2016) Tool. Mean nutrient intakes were compared with Dietary Reference Intakes. Food items (excluding supplements) were extracted and categorized according to the Bureau of Nutritional Sciences Food Group Codes. Percentages of total dietary intakes from food sources were ranked. Grain products were the highest ranked contributor of energy (21.4%), carbohydrate (30.3%), fibre (29.1%), and iron (35.3%). Breakfast cereals, hot cereals, yeast breads, and mixed grain dishes (mainly rice or pasta-based) were the most important nutrient contributors for grains, despite most (64.3%) commercial cereals and breads being unenriched. Legumes and seeds were not frequently consumed. Nutrient density in the GFD could be improved with more emphasis on gluten-free (GF) whole grains, legumes, seeds, and enriched breads and cereals. More research is needed on the nutrient composition of GF foods to identify food sources of folate, other B vitamins, zinc and magnesium—nutrients of concern for those requiring a GFD.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".