The Effect of Vitamin D Supplementation on Prostate Cancer: A Systematic Review and Meta-Analysis of Clinical Trials
Bibliographic record
Abstract
Vitamin D has received attention for its potential to disrupt cancer processes. However, its effect in the treatment of prostate cancer is controversial. This study aimed to assess the effect of vitamin D supplementation on patients with prostate cancer. In the present study, PubMed, Scopus, ISI Web of Science, and Google Scholar were searched up to September 2017 for trials that evaluated the effect of vitamin D supplementation on prostate specific antigen (PSA) response, mortality, and its possible side effects in participants with prostate cancer. The DerSimonian and Laird inverse-weighted random-effects model was used to pool the effect estimates. Twenty-two studies (16 before-after and 6 randomized controlled trials) were found and included in the meta-analysis. The analysis of controlled clinical trials revealed that PSA change from baseline [weighted mean difference (WMD)=-1.66 ng/ml, 95% CI: -0.69, 0.36, p=0.543)], PSA response proportion (RP=1.18, 95% CI: 0.97, 1.45, p=0.104) and mortality rate (risk ratio (RR)=1.05, 95% CI: 0.81-1.36; p=0.713) were not significantly different between vitamin D supplementation and placebo groups. Single arm trials revealed that vitamin D supplementation had a modest effect on PSA response proportion: 19% of those enrolled had at least a 50% reduction in PSA by the end of treatment (95% CI: 7% to 31%; p=0.002). Although before-after studies showed that vitamin D increases the PSA response proportion, it does not seem that patients with prostate cancer benefit from high dose vitamin D supplementation and it should not be recommended for the treatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.036 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".