Prevalence and predictors of low vitamin D concentrations in urban Canadian toddlers
Bibliographic record
Abstract
OBJECTIVES: To determine the prevalence of low vitamin D concentrations in a cohort of healthy two-year-old children living in a large Canadian city, and to explore whether body mass index (BMI) and cow's milk intake are associated with low vitamin D concentrations. METHODS: A cross-sectional study was performed on healthy two-year-old children attending a well-child visit in Toronto, Ontario (latitude 43.4°N). Dietary exposures were measured by questionnaire. The primary outcome was the prevalence of low vitamin D concentrations (25-hydroxyvitamin D concentration of lower than 50 nmol/L or lower than 75 nmol/L). RESULTS: A total of 91 healthy children 24 to 30 months of age were recruited between November 2007 and May 2008. The prevalence of low vitamin D concentrations (lower than 50 nmol/L) was 32% (29 of 92, 95% CI 22% to 42%) and the prevalence of vitamin D concentrations of lower than 75 nmol/L was 82% (75 of 91, 95% CI 73% to 89%). Using multivariable logistic regression, the odds of vitamin D concentrations being lower than 50 nmol/L decreased by 0.44 (95% CI 0.2 to 0.96) for each additional cup of cow's milk intake per day and increased by 1.2 to 2.6 per unit BMI depending on BMI level (P=0.07). CONCLUSIONS: A total of 30% to 80% of toddlers in the present study's urban Canadian setting demonstrated low vitamin D concentrations - the highest prevalence of low vitamin D in toddlers outside of Alaska. Modifiable factors associated with low vitamin D were lower cow's milk intake and higher BMI. The vitamin D status of toddlers in urban Canada may require specific attention.
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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 | 0.000 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".