Zinc-Fortified Foods Do Not Improve Intake of Total Dietary Zinc for Ontario Preschoolers
Bibliographic record
Abstract
OBJECTIVE: To examine if zinc food fortification makes a significant contribution to dietary zinc intake and to describe zinc-fortified food usage, amongst Canadian preschoolers. METHODS: Cross-sectional data were used from the NutriSTEP validation project for which preschoolers (3-5 years) from across Ontario were recruited using convenience sampling. Three-day food records were used to estimate mean daily zinc intake and children were stratified by age group for analysis. Comparisons were then made to the Dietary Reference Intakes, whilst accounting for zinc from zinc-fortified foods and supplements and also whilst excluding zinc from zinc-fortified foods, to determine the prevalence of inadequate (< Estimated Average Requirement (EAR)) and excessive (> Tolerable Upper Intake Level (TUL)) zinc intakes. The contributions (%) made to total zinc intake by zinc-fortified foods, unfortified foods and zinc-containing supplements were determined as were contributions made to zinc intake by zinc-fortified foods, categorized by fortified-food type. RESULTS: Fewer than a third (30%, n = 76) of participants ate zinc-fortified foods and only 3% (n = 7) used a zinc-containing supplement. Including the contribution from zinc-fortified foods, 25% (n = 32) of 3-year-olds (n = 128) had mean zinc intake (range 7.0-7.6 mg/day) marginally above the TUL (7 mg/day). Zinc-fortified foods contributed only 2.3 +/- 5.8% (mean% +/- SD) to total zinc intake. The mean intake of the 25% of 3-year-olds above the TUL is attributed to their higher mean energy and protein intakes (p < 0.001) as compared to 3-year-olds not consuming zinc at levels above the TUL. Even excluding zinc-fortified foods, the prevalence of inadequate zinc intakes (
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".