Usual vitamin D intake in the United States by ethnic group: NHANES 2003–04
Bibliographic record
Abstract
Background – Current food consumption survey databases do not include vitamin D, thus there are limited data on intakes. Objective ‐ To determine usual intake of vitamin D, to evaluate ethnic differences in vitamin D intake, and estimate the percentage of population subgroups whose usual intakes were at or above Adequate Intake (AI). Methods – Vitamin D content of survey foods were estimated using the Food and Nutrition Database for Dietary Studies 2.0 recipe database, and vitamin D food composition data of the Standard Reference, Release 19. 2‐day average vitamin D intakes (mean ± SE) of ethnic groups were compared using SUDAAN, and C‐SIDE was used to estimate parameters of the usual intake distribution in NHANES 2003–04 (n=8213). Results – Mean vitamin D intakes (and % at or above AI) were 253±13 (43%), 236±13 (29%) and 206±11 IU/day (20%) for children 1–3, 4–8 and 9–13 yr, respectively. Estimates for males vs. females of 14–18 yr were: 228±16 (24%) vs. 158±13 (10%); 19–30 yr: 180±11 (11%) vs. 130±11 (7%); 31–50 yr: 180±14 (11%) vs. 144±7 (9%); 51–70 yr: 201±20 (3%) vs. 161±14 (2%); >70 yr: 233±22 (2%) vs. 179±14 IU/day (1%). Vitamin D intakes were lower in blacks than whites for children 1–3, 4–8, 9–13, 14–18 yr, males 31–50, females 51–70 and males >70 yr. Conclusions – AI levels are 200, 400 and 600 IU/day vitamin D for 1–50, 51–70, and >70 yrs, respectively. Thus, using food databases mentioned above, usual intakes of vitamin D were low compared to the AI in the US, especially in blacks, females and older age groups. (Supported by Kraft Foods, Inc.)
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".