Global prevalence and disease burden of vitamin D deficiency: a roadmap for action in low‐ and middle‐income countries
Bibliographic record
Abstract
Vitamin D is an essential nutrient for bone health and may influence the risks of respiratory illness, adverse pregnancy outcomes, and chronic diseases of adulthood. Because many countries have a relatively low supply of foods rich in vitamin D and inadequate exposure to natural ultraviolet B (UVB) radiation from sunlight, an important proportion of the global population is at risk of vitamin D deficiency. There is general agreement that the minimum serum/plasma 25-hydroxyvitamin D concentration (25(OH)D) that protects against vitamin D deficiency-related bone disease is approximately 30 nmol/L; therefore, this threshold is suitable to define vitamin D deficiency in population surveys. However, efforts to assess the vitamin D status of populations in low- and middle-income countries have been hampered by limited availability of population-representative 25(OH)D data, particularly among population subgroups most vulnerable to the skeletal and potential extraskeletal consequences of low vitamin D status, namely exclusively breastfed infants, children, adolescents, pregnant and lactating women, and the elderly. In the absence of 25(OH)D data, identification of communities that would benefit from public health interventions to improve vitamin D status may require proxy indicators of the population risk of vitamin D deficiency, such as the prevalence of rickets or metrics of usual UVB exposure. If a high prevalence of vitamin D deficiency is identified (>20% prevalence of 25(OH)D < 30 nmol/L) or the risk for vitamin D deficiency is determined to be high based on proxy indicators (e.g., prevalence of rickets >1%), food fortification and/or targeted vitamin D supplementation policies can be implemented to reduce the burden of vitamin D deficiency-related conditions in vulnerable populations.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.017 | 0.007 |
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".