Safety, Feasibility, and Biomarker Effects of High-Dose Vitamin D Supplementation Among Women at High Risk for Breast Cancer
Bibliographic record
Abstract
Vitamin D deficiency is a potentially modifiable risk factor that may be targeted for breast cancer prevention.We examined the safety, feasibility, and biomarker effects of high-dose vitamin D among women at high risk for breast cancer.Forty high-risk women, defined as a 5-year breast cancer risk ≥1.67% per the Gail model, lobular or ductal carcinoma in situ, were assigned to a 1-year intervention of vitamin D3 20,000 IU or 30,000 IU weekly.Participants were monitored for toxicity every 3 months, underwent serial blood draws at baseline, 6 and 12 months, and a digital mammogram at baseline and 12 months.Biomarker endpoints included serum 25-hydroxyvitamin D [25(OH)D], 1,25-dihydroxyvitamin D [1,25(OH) 2 D], parathyroid hormone (PTH), insulin-like growth factor (IGF-1), IGF binding protein (IGFBP-3), and mammographic density (MD) using Cumulus software.From November 2007 to January 2011, we enrolled 40 women; 37 were evaluable at 6 months and 30 at 12 months.One patient was taken off study for hypercalciuria; otherwise, the intervention was well tolerated.From baseline to 12 months, mean serum 25(OH)D and 1,25(OH) 2 D rose from 20.0 to 46.9 ng/ml and 69.7 to 98.1 pg/ml, respectively (p<0.01).Serum PTH decreased by 12% at 6 months and IGF-1/IGFBP-3 ratio decreased by 4.3% at 12 months (p<0.05).There was no significant change in MD regardless of menopausal status or dose level.We demonstrated that 1 year of high-dose vitamin D3 was associated with a significant increase in circulating vitamin D levels and favorable effects on IGF signaling, but no significant change in MD.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".