High‐dose oral vitamin D3 administration increases serum and prostate levels of vitamin D metabolites safely in prostate cancer patients
Bibliographic record
Abstract
Objective To characterize the effect of high doses of vitamin D (VD) on its metabolite levels in prostate tissue. Methods Vitamin D3 doses (400‐, 10,000‐, or 40,000 IU/d) were randomized equally to 48 prostate cancer patients for 3–8 wk prior to radical prostatectomy. Surgical prostate tissue and serum were assayed for 25‐hydroxyvitamin D3 [25(OH)D3], 1,25‐dihydroxyvitamin D [1,25(OH)2D], and VD binding protein (DBP). Free VD metabolite indices were calculated as the ratio between VD metabolite and DBP concentrations. Plasma and urine calcium were monitored for safety. Results Serum 25(OH)D3 increased dose‐dependently over time (p<0.01). Serum 1,25(OH)2D increased with 10,000‐and 40,000 IU/d only (p<0.01). Prostate 25(OH)D3 and 1,25(OH)2D, as well as their free indices, were higher with 40,000 IU/d compared to the other doses (p<0.02). Free 25(OH)D3 and 1,25(OH)2D indices were significantly higher in prostate than serum (p<0.001). Calcium levels did not increase adversely with dosing. After dosing (mean ± SD) 400 IU/d 10,000 IU/d 40,000 IU/d P (ANOVA) Serum 25(OH)D3 (nmol/L) 69 ± 15 128 ± 30 296 ± 69 <0.001 Serum 1,25(OH)2D (pmol/L) 119 ± 45 143 ± 36 176 ± 40 <0.001 Prostate 25(OH)D3 (nmol/kg) 94 ± 24 116 ± 27 179 ± 72 <0.001 Prostate 1,25(OH)2D (pmol/kg) 29 ± 12 29 ± 10 42 ± 19 0.01 Conclusion High vitamin D3 intake produced the elevated levels of VD metabolites within prostate hypothesized to protect against cancer. Grant Funding Source : Canadian Cancer Society
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 | 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".