Prevalence and predictors of low serum 25-hydroxyvitamin D levels in rural Canadian children
Bibliographic record
Abstract
OBJECTIVES: Studies in Canada have reported varying prevalences of low serum 25-hydroxyvitamin D (25(OH)D) levels, but none have been conducted in rural paediatric populations. The purpose of this study was to determine the prevalence and predictors of low vitamin D levels in rural communities. METHODS: We conducted a cross-sectional study of children aged 3 to 15 living in Canadian Hutterite communities. Serum 25(OH)D levels were measured between October 2008 and April 2009 using a chemiluminescence assay. Predictors of vitamin D levels were evaluated using multivariable linear regression. A multilevel model was used to evaluate the impact of individual, household and colony factors on the variation in vitamin D levels. RESULTS: Serum 25(OH)D levels were available on 743 children/adolescents. The median was 62.0 nmol/L (interquartile range 51.0, 74.0). Levels lower than 50 nmol/L and 75 nmol/L were found in 152 (20.5%) and 565 (76%) children, respectively. Adolescents were at highest risk for levels <75 nmol/L (odds ratio 3.38, 95% confidence interval 2.00, 5.80). Age and latitude were negatively correlated with serum 25(OH)D level. In the multilevel model, most of the variation in levels was associated with individual children. CONCLUSION: Low vitamin D levels are a significant problem in rural Hutterite communities in Canada. Adolescents were at greatest risk for low levels and represent an important target group for supplementation. Variation in serum 25(OH)D levels was explained mostly at the individual level. Additional studies are needed to explore factors associated with individuals (e.g., genetics) leading to lower 25(OH)D levels.
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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".