Dietary vitamin D intake and food sources of US and Canadian population
Bibliographic record
Abstract
Objective was to determine vitamin D intake and food sources in US (n=7837) and Canadian (n=4025) populations using 7‐ or 14‐d food intake data from a nationally representative population. Mean vitamin D intake ranged from 3.7 to 6.0 mcg/d. One‐third of the 2–50 y age groups met their AI for vitamin D, except US women, 19–50 y, where only 23% met their AI. Less than 10% of individuals over the age of 50 y met their AI for vitamin D. The top 10 food sources of vitamin D in the diets of U.S. participants were milk, meat, fish, eggs, ready‐to‐eat (RTE) cereal, pasta and rice, dairy and non‐dairy frozen desserts, poultry, shellfish, and vegetable recipes; among the Canadian participants they were milk, meat, fish, margarine, eggs, egg recipes, pasta and rice, dairy and non‐dairy frozen desserts, vegetable recipes, and soups. Participants who frequently consumed RTE cereal had significantly higher total vitamin D intake vs. those who did not consume or were infrequent consumers. Frequent breakfast consumers had significantly higher vitamin D intake vs. those who did not consume breakfast or were infrequent consumers. Given the inadequate intake of vitamin D, emphasis needs to be put on increasing dietary sources of vitamin D, including vitamin D fortified foods, to help individuals meet the vitamin D dietary recommendations. Funding: General Mills Bell Institute of Health and Nutrition, Minneapolis, MN
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".