Statin medication use and the risk of biochemical recurrence after radical prostatectomy
Bibliographic record
Abstract
BACKGROUND: Although controversial, evidence suggests statins may reduce the risk of advanced prostate cancer (PC), and recently statin use was associated with prostate-specific antigen (PSA) reductions among men without PC. The authors sought to examine the association between statin use and PSA recurrence after radical prostatectomy (RP). METHODS: The authors examined 1319 men treated with RP from the Shared Equal Access Regional Cancer Hospital (SEARCH) Database. Time to PSA recurrence was compared between users and nonusers of statin at surgery using Cox proportional hazards models adjusted for multiple clinical and pathological features. RESULTS: In total, 236 (18%) men were taking statins at RP. Median follow-up was 24 months for statin users and 38 for nonusers. Statin users were older (P<.001) and underwent RP more recently (P<.001). Statin users were diagnosed at lower clinical stages (P=.009) and with lower PSA levels (P=.04). However, statin users tended to have higher biopsy Gleason scores (P=.002). After adjusting for multiple clinical and pathological factors, statin use was associated with a 30% lower risk of PSA recurrence (hazard ratio "HR", 0.70; 95% confidence interval "CI", 0.50-0.97; P=.03), which was dose dependent (relative to no statin use; dose equivalent simvastatin 20 mg: HR, 0.50; 95% CI, 0.27-0.93; P=.03). CONCLUSIONS: In this cohort of men undergoing RP, statin use was associated with a dose-dependent reduction in the risk of biochemical recurrence. If confirmed in other studies, these findings suggest statins may slow PC progression after RP.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".