Prospective Study of Fruit and Vegetable Intake and Risk of Prostate Cancer
Bibliographic record
Abstract
BACKGROUND: Several epidemiologic studies have reported associations between fruit and vegetable intake and reduced risk of prostate cancer, but the findings are inconsistent and data on clinically relevant advanced prostate cancer are limited. METHODS: We evaluated the association between prostate cancer risk and intake of fruits and vegetables in 1338 patients with prostate cancer among 29,361 men (average follow-up = 4.2 years) in the screening arm of the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial. Participants completed both a general risk factor and a 137-item food-frequency questionnaire at baseline. Cox proportional hazards models were used to estimate relative risks (RRs) and 95% confidence intervals (CIs). All statistical tests were two-sided. RESULTS: Vegetable and fruit consumption was not related to prostate cancer risk overall; however, risk of extraprostatic prostate cancer (stage III or IV tumors) decreased with increasing vegetable intake (RR = 0.41, 95% CI = 0.22 to 0.74, for high versus low intake; P(trend) = .01). This association was mainly explained by intake of cruciferous vegetables (RR = 0.60, 95% CI = 0.36 to 0.98, for high versus low intake; P(trend) = .02), in particular, broccoli (RR = 0.55, 95% CI = 0.34 to 0.89, for >1 serving per week versus <1 serving per month; P(trend) = .02) and cauliflower (RR = 0.48, 95% CI = 0.25 to 0.89 for >1 serving per week versus <1 serving per month; P(trend) = .03). We found some evidence that risk of aggressive prostate cancer decreased with increasing spinach consumption, but the findings were not consistently statistically significant when restricted to extraprostatic disease. CONCLUSION: High intake of cruciferous vegetables, including broccoli and cauliflower, may be associated with reduced risk of aggressive prostate cancer, particularly extraprostatic disease.
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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".