Nutritional aspects of prostate cancer: a review.
Bibliographic record
Abstract
OBJECTIVES: The primary prevention of prostate cancer through nutritional modification is becoming a focus of attention as important relationships between diet and cancer are becoming evident. Relevant research is reviewed, along with recent data implicating various vitamin supplements and food products in the prevention and treatment of prostate cancer. METHODS: The epidemiology of prostate cancer, and current knowledge of prevention, screening, and progression of neoplasia is discussed. The current understanding of diet and its importance in primary and secondary prevention is explored. Literature searches were performed on MedLine using relevant keywords to find studies relating to prevention and treatment of prostate cancer using dietary methods. Of these, 104 published manuscripts were used. The search was limited from the year 1975 to the present. RESULTS: Incidence rates for prostate cancer vary according to diet and lifestyle. Several double-blind placebo-controlled clinical trials have shown that supplementation with selenium reduces cancer incidence. Inhibitory effects on the growth of in vitro prostate cancer cell lines have been observed with the administration of soy isoflavones, lycopenes from tomatoes, and vitamin D. Other compounds, such as calcium and fatty acids, have been linked to higher incidences of prostate cancer. CONCLUSIONS: Evidence exists that diet may play an important role in the primary prevention of prostate cancer. Further research is necessary to define the role that nutrition plays in the prevention or promotion of prostate cancer.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".