The Relationship Between Cow’s Milk and Stores of Vitamin D and Iron in Early Childhood
Bibliographic record
Abstract
OBJECTIVE: To examine the association between cow's milk intake on both vitamin D and iron stores in healthy urban preschoolers. METHODS: Healthy children 2 to 5 years of age were recruited from December 2008 through December 2010 through the TARGet Kids! practice-based research network. Cow's milk intake was measured by parental report. Vitamin D and iron stores were measured by using serum 25-hydroxyvitamin D and ferritin. Bivariate multivariable linear regression was used to examine the effect of cow's milk intake simultaneously on 25-hydroxyvitamin D and serum ferritin. Analyses were stratified by important clinical variables including skin pigmentation, bottle feeding, vitamin D supplementation, and season. RESULTS: Among 1311 children, increasing cow's milk consumption was associated with decreasing serum ferritin (P < .0001) and increasing 25-hydroxyvitamin D (P ≤ .0001). Two cups (500 mL) of cow's milk per day maintained 25-hydroxyvitamin D >75 nmol/L with minimal negative effect on serum ferritin for most children. Children with darker skin pigmentation not receiving vitamin D supplementation during the winter required 3 to 4 cups of cow's milk per day to maintain 25-hydroxyvitamin D >75 nmol/L. Cow's milk intake among children using a bottle did not increase 25-hydroxyvitamin D and resulted in more dramatic decreases in serum ferritin. CONCLUSIONS: There is a trade-off between increasing 25-hydroxyvitamin D and decreasing serum ferritin with increasing milk intake. Two cups of cow's milk per day appears sufficient to maintain healthy vitamin D and iron stores for most children. Wintertime vitamin D supplementation was particularly important among children with darker skin pigmentation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".