Food sources of energy and nutrients among Canadian adults following a gluten-free diet
Bibliographic record
Abstract
BACKGROUND: The gluten-free diet (GFD) involves the elimination of wheat and related grains. Wheat is a key fortification vehicle for nutrients such as iron and B vitamins. While there is growing evidence of low nutrients intake and poor diet quality amongst people following long-term GFD, few studies have used a dietary pattern approach to analyse top food sources of nutrients in today's complex food environment. Thus, the purpose of this study was to identify food sources of energy and nutrients from previously collected diet records of adults following a GFD. METHODS: = 240 records). All food items were categorised according to the Bureau of Nutritional Sciences Food Group Codes. Percentages of total dietary intakes from food groups were ranked. RESULTS: Mean intakes of dietary fibre, calcium and iron (females) were lower than recommended, with half the sample consuming below the recommended proportion of energy as carbohydrate. Meat, poultry and fish were the top source of energy (19.5%) in the diet. Gluten-free (GF) grain products were the top source of carbohydrate, fibre and iron and second greatest source of energy. Amongst grains, breakfast/hot cereals, yeast breads, and mixed grain dishes were the greatest nutrient contributors, despite most commercial cereals and breads (65%) being unenriched. Legumes were not frequently consumed. CONCLUSIONS: GF grains were the top food source of carbohydrate, fibre and iron, despite few brands being enriched or fortified. It is a challenge to assess and monitor nutrient intakes on GFD due to the lack of nutrient composition data for B vitamins and minerals (other than iron). Dietary planning guidance for the appropriate replacement of nutrients provided by wheat is warranted.
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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 | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".