Impact of putative chemopreventative agents on prostate cancer diagnosis.
Bibliographic record
Abstract
40 Background: Prostate cancer (PC) is the most common non-cutaneous cancer in Canadian men and the third most common cause of cancer death in Canada. Several studies have shown that use of commonly prescribed medications, including those used for diabetes and hypercholesterolemia, is associated with improved survival in various malignancies, including PC. There has not been any large population-based study, examining the effects of these and other commonly prescribed medications, on the rate of PC diagnosis, over a 20 years follow-up period. Methods: A retrospective population-based study using data from the institute of clinical evaluative sciences, including all male patients aged 65 and above in Ontario who have had a negative first prostate biopsy between 1994 and 2016. We assessed the impact of commonly prescribed medications on PC diagnosis. The medications included Statins (hydrophilic and hydrophobic), diabetes drugs (metformin, insulins, sulfonylureas, and thizolidinedions), proton pump inhibitors, 5 alpha reductase inhibitors, and alpha blockers. Time dependent Cox regression proportional hazards models were performed determine predictors of PC diagnosis. Medication exposure was time varying and modeled as “ever” vs. “never” use or as cumulative exposure for 6 months of usage. A priori variables included in the model included age, ADG comorbidity score, rurality index, index year, and all medications. Results: A total of 51,415 men were analyzed over a mean (SD) follow-up time of 8.06 (5.44) years. Overall, 10,466 patients (20.4%) were diagnosed with PC, 16,726 (32.5%) had died, and 1,460 (2.8%) patients died of PC. On multivariable analysis increasing age and rurality index were associated with higher PC diagnosis rate, while a more recent index year, and usage of hydrophilic statins was associated with a lower diagnosis rate in both “ever” vs. “never” and cumulative models (HR 0.832, 95% CI 0.732-0.946, p = 0.005, HR 0.973 95% CI 0.951-0.995, p = 0.016, respectively). Conclusions: Hydrophilic statins are associated with a clinically significant lower PC diagnosis. To our knowledge this is the first study demonstrating a clear advantage of one group of statins (hydrophilic) over another (hydrophobic) in PC 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 | 0.001 | 0.006 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".