Do sufficient vitamin D levels at the end of summer in children and adolescents provide an assurance of vitamin D sufficiency at the end of winter? A cohort study
Bibliographic record
Abstract
BACKGROUND: The changes in serum 25-hydroxyvitamin D (25(OH)D) in adolescents from summer to winter and optimal serum vitamin D levels in the summer to ensure adequate vitamin D levels at the end of winter are currently unknown. This study was conducted to address this knowledge gap. METHODS: The study was conducted as a cohort study. Sixty-eight participants aged 7-18 years and who had sufficient vitamin D levels at the end of the summer in 2011 were selected using stratified random sampling. Subsequently, the participants' vitamin D levels were measured at the end of the winter in 2012. A receiver operating characteristic (ROC) curve was used to determine optimal cutoff points for vitamin D at the end of the summer to predict sufficient vitamin D levels at the end of the winter. RESULTS: The results indicated that 89.7% of all the participants had a decrease in vitamin D levels from summer to winter: 14.7% of them were vitamin D-deficient, 36.8% had insufficient vitamin D concentrations and only 48.5% where able to maintain sufficient vitamin D. The optimal cutoff point to provide assurance of sufficient serum vitamin D at the end of the winter was 40 ng/mL at the end of the summer. Sex, age and vitamin D levels at the end of the summer were significant predictors of non-sufficient vitamin D at the end of the winter. CONCLUSIONS: In this age group, a dramatic reduction in vitamin D was observed over the follow-up period. Sufficient vitamin D at the end of the summer did not guarantee vitamin D sufficiency at the end of the winter. We found 40 ng/mL as an optimal cutoff point.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".