Progression From High-Grade Prostatic Intraepithelial Neoplasia to Cancer: A Randomized Trial of Combination Vitamin-E, Soy, and Selenium
Bibliographic record
Abstract
PURPOSE: High-grade prostatic intraepithelial neoplasia (HGPIN) is a putative precursor of invasive prostate cancer (PCa). Preclinical evidence suggests vitamin E, selenium, and soy protein may prevent progression of HGPIN to PCa. This hypothesis was tested in a randomized phase III double-blind study of daily soy (40 g), vitamin E (800 U), and selenium (200 μg) versus placebo. PATIENTS AND METHODS: Three hundred three men in 12 Canadian centers were analyzed. The main eligibility criterion was confirmed HGPIN in at least one of two biopsies within 18 months of random assignment. Treatment was administered daily for 3 years. Follow-up prostate biopsies occurred at 6, 12, 24, and 36 months postrandomization. The primary end point was time to development of invasive PCa. Kaplan-Meier plots and log-rank tests were used to compare two treatment groups for this end point. RESULTS: For all patients, the median age was 62.8 years. The median baseline prostate-specific antigen (PSA; n = 302) was 5.41 ug/L; total testosterone (n = 291) was 13.4 nmol/L. Invasive PCa developed among 26.4% of patients. The hazard ratio for the nutritional supplement to prevent PCa was 1.03 (95% CI, 0.67 to 1.60; P = .88). Gleason score distribution was similar in both groups with 83.5% of cancers graded Gleason sum of 6. Baseline age, weight, PSA, and testosterone did not predict for development of PCa. The supplement was well tolerated with flatulence reported more frequently (27% v 17%) among men receiving micronutrients. CONCLUSION: This trial does not support the hypothesis that combination vitamin E, selenium, and soy prevents progression from HGPIN to PCa.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".