How Much Vitamin D Do We Need for Skeletal Health?
Bibliographic record
Abstract
BACKGROUND: Vitamin D is critical for musculoskeletal health and has been implicated in the risk of extraskeletal diseases, including cancer, cardiovascular diseases, and autoimmune diseases, as well as overall mortality. Although numerous studies deal and have dealt with vitamin D deficiency and its consequences, experts cannot agree on the right 25-hydroxyvitamin D levels. This survey aims to shed light on the ongoing vitamin D controversy from different angles. QUESTIONS/PURPOSES: We discuss the minimum threshold for the 25-hydroxyvitamin D level to guarantee optimal health, why vitamin is D critical to musculoskeletal and extraskeletal functions, and new evidence for the success of prevention measures such as food fortification. METHODS: We searched PubMed, Google Scholar, and reference lists of articles using several keywords. The most recent search was in February 2011. RESULTS: While the use of parathyroid hormone as a surrogate measure did not lead to a consensus concerning the required 25-hydroxyvitamin D serum level, the combined analysis of bone mineralization and vitamin D status has established minimum levels of more than 75 nmol/L (30 ng/mL) to guarantee at least skeletal health. An effective measure to approach this status is food fortification, which has been demonstrated by countries such as Canada, the United States, and Finland. CONCLUSIONS: Given the health economic implications of failure to maintain a balanced vitamin D status, action is recommended to integrate current scientific knowledge on vitamin D into physicians' treatment of patients and governmental policies on food fortification.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".