Examining the effects of increased vitamin D fortification on dietary inadequacy in Canada
Bibliographic record
Abstract
Despite mandatory fortification of milk and margarine, most Canadians have inadequate vitamin D intake and consequently poor vitamin D status. Inclusion of more foods to be fortified and increasing the levels of fortification are possible strategies to address this inadequacy. We used dietary intakes (24‐h recall) from the 2004 Canadian Community Health Survey 2.2 (n = 34,381) to model the effectiveness of increased vitamin D fortification by determining the prevalence of vitamin D inadequacy and percent of intakes >;Tolerable Upper Intake Level (UL) based on several fortification scenarios. Doubling of milk fortification, and fortification of yogurt and cheese at 6.75 μg/serving led to more than doubling of vitamin D intakes across all sex/age groups and a drop in the prevalence of inadequacy from >;80% to <50% in all groups. Furthermore, no intakes approach the UL under any fortification scenario in any sex/age group. Given the pressing need to improve vitamin D status among Canadians, and the fact that increasing vitamin D in dairy products can lead to increased intake without a risk of excess, increased fortification is a population wide strategy that should be given consideration in Canada. (Funded by the Dairy Farmers of Canada)
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 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".