Review of recent evidence in support of a role for statins in the prevention of prostate cancer
Bibliographic record
Abstract
PURPOSE OF REVIEW: We examine the potential chemopreventive role statins may have in prostate cancer, highlight the basic science supporting this role and analyze the human data regarding the association between statin use and prostate cancer. RECENT FINDINGS: Basic scientific evidence suggests that, through cholesterol and noncholesterol-mediated mechanisms, statins inhibit many pathways of cancer formation and progression. A handful of observational studies found statin use was associated with reduced prostate cancer risk, though others found no association. In the last year, however, four large prospective studies have observed similar reductions in the risk of advanced prostate cancer with essentially no reduction in the risk of overall prostate cancer. This may, in part, explain why previous studies, including large metaanalyses of clinical trials of statins in the prevention of cardiovascular outcomes, did not observe any association between statin use and overall prostate cancer risk. SUMMARY: The exact association between statin medication use and prostate cancer, and whether this association is causal in nature, remains unclear. Recent evidence, however, is encouraging, particularly for reducing the risk of advanced disease. Thus, while at present there are insufficient data to recommend all men start taking a statin medication regardless of their cholesterol profile, the rationale to move forward with further research is clear.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".