Assessment of Nutritional Adequacy of Packaged Gluten-free Food Products
Bibliographic record
Abstract
PURPOSE: There is concern about the nutritional quality of processed gluten-free (GF) products. The aim was to investigate the nutrient composition and cost of processed GF products compared with similar regular products. METHODS: Product size, price, caloric value, and macro- and micronutrient composition were compared between foods labeled "Gluten-free" and comparable regular products in 5 grocery stores in 3 Canadian cities. Data were calculated per 100 g of product. RESULTS: A total of 131 products were studied (71 GF, 60 regular). Overall, calories were comparable between GF and regular foods. However, fat content of GF breads was higher (mean 7.7 vs. 3.6 g, P = 0.003), whereas protein was lower (mean 5.0 vs. 8.0 g, P = 0.001). Mean carbohydrate content of GF pasta was higher (78 vs. 74 g, P = 0.001), whereas protein (7.5 vs. 13.3 g, P < 0.001), fibre (3.3 vs. 5.8 g, P = 0.048), iron (9% vs. 25%DV, P < 0.001), and folate content (5% vs. 95%DV, P < 0.001) were lower. Mean price of GF products was $1.99 versus $1.23 for regular products (P < 0.001). CONCLUSIONS: Some commonly consumed packaged GF foods are higher in fat and carbohydrates and lower in protein, iron, and folate compared with regular products. GF products are more expensive. Dietitians should counsel patients on the GF diet regarding its nutritional and financial impact.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".