Chemoprevention of Prostate Cancer: Myths and Realities
Bibliographic record
Abstract
BACKGROUND: Prostate cancer will affect 15% to 18% of men in North America and will result in death in 3%. Established curative and palliative treatments for prostate cancer are associated with significant morbidity and cost. For these reasons, prostate cancer is an ideal target for prevention. METHODS: Using MEDLINE we performed a systematic review of clinical trials that have investigated pharmaceutical or nutritional interventions for the prevention of prostate cancer. The available evidence was critically evaluated and summarized according to the strength of recommendation taxonomy. RESULTS: Many pharmaceutical and nutritional interventions have been investigated for the prevention of prostate cancer. The strongest evidence exists to support the use of 5 α-reductase inhibitors (5-ARIs) for prevention of prostate cancer. However, the evidence is insufficient to recommend that these agents be used routinely among all men. In addition, the optimal timing or duration of 5-ARI use in not known. At present there is no suitable evidence to recommend using any specific nutritional supplement or diet to prevent prostate cancer. CONCLUSIONS: Prostate cancer prevention should not be offered systematically to all men. There may be a role for 5-ARI use among motivated men who wish to take a proactive approach to prostate cancer prevention.
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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 | 0.012 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".