Voluntarily Fortified Foods in the United States: Consumer Characteristics and Potential for Excessive Intakes
Bibliographic record
Abstract
Introduction The addition of vitamins and minerals to foods voluntarily is expanding in the US, as evidenced by the emergence of new fortified foods (FF), such as vitamin waters. Existing literature suggests that voluntarily FF contribute significantly to nutrient intakes, however in the context of widespread supplement use, there is a need to examine the impact of FF on excessive intakes. Objectives Our objectives were to determine: 1) the characteristics of FF consumers in the US and 2) whether FF consumption increases the likelihood of nutrient intakes above the tolerable upper intake level (UL). Methods Using the National Health and Nutrition Examination Survey (2007–08) we identified FF consumers on each of 2 24hr dietary recalls. Logistic regression was used to determine the characteristics of FF consumers and whether FF consumers were more likely to exceed the UL from foods and supplements. Results Almost half of the population consumed at least one FF on either recall day. FF consumers were more likely to take dietary supplements and be non‐smokers. FF consumers were more likely to exceed the UL for zinc, niacin, folic acid and retinol than non‐consumers. This relationship was most often observed for young children. Conclusions With expanding voluntary FF, careful monitoring of this practice is needed, particularly given the observed elevated risk of nutrient excess. Grant Funding Source : CIHR Doctoral Research Award
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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 | 0.000 | 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".