MECHANISMS IN ENDOCRINOLOGY: Vitamin D as a potential contributor in endocrine health and disease
Bibliographic record
Abstract
OBJECTIVE: It has been suggested that vitamin D may play a role in the pathogenesis of several endocrine diseases, such as hyperparathyroidism, type 1 diabetes (T1DM), type 2 diabetes (T2DM), autoimmune thyroid diseases, Addison's disease and polycystic ovary syndrome (PCOS). In this review, we debate the role of vitamin D in the pathogenesis of endocrine diseases. METHODS: Narrative overview of the literature synthesizing the current evidence retrieved from searches of computerized databases, hand searches and authoritative texts. RESULTS: Evidence from basic science supports a role for vitamin D in many endocrine conditions. In humans, inverse relationships have been reported not only between blood 25-hydroxyvitamin D and parathyroid hormone concentrations but also with risk of T1DM, T2DM, and PCOS. There is less evidence for an association with Addison's disease or autoimmune thyroid disease. Vitamin D supplementation may have a role for prevention of T2DM, but the available evidence is not consistent. CONCLUSIONS: Although observational studies support a potential role of vitamin D in endocrine disease, high quality evidence from clinical trials does not exist to establish a place for vitamin D supplementation in optimizing endocrine health. Ongoing randomized controlled trials are expected to provide insights into the efficacy and safety of vitamin D in the management of endocrine disease.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".