IMPACT of putative chemopreventative agents on prostate cancer diagnosis.
Bibliographic record
Abstract
e16553 Background: Prostate cancer (PC) is the most common non-cutaneous cancer in men and the third most common cause of cancer death in males. Several studies have shown that use of commonly prescribed medications, 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, such as proton pump inhibitors (PPI), on the rate of PC diagnosis, PC advanced disease and PC-specific death. 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 outcomes. The analyzed medications included Statins (hydrophilic and hydrophobic), most commonly used diabetes drugs (metformin, insulins, sulfonylureas, and thizolidinedions), PPIs, 5 alpha reductase inhibitors, and alpha blockers. Time dependent Cox regression proportional hazards models were performed to determine predictors of PC diagnosis, PC advanced disease (defined as usage of hormonal therapy), and PC-specific death. Medication exposure was time varying and modelled as “ever” vs. “never” use or as cumulative exposure. Results: A total of 21,562 men were analyzed over a mean (SD) follow-up time of 8.06 (5.44) years. Overall, 5,187 patients (24%) were diagnosed with PC, 7861 (36.5%) had died, and 647 (3%) died of PC. On multivariable analysis usage of hydrophilic statins modelled as “ever vs. never” was associated with a lower diagnosis rate (OR 0.832, 95% CI 0.732-0.946, p = 0.005) and a significantly decreased PC-specific death (OR 0.676, 95% CI 0.528-0.871, p = 0.0024). In contrast, Pantoprazole was associated with a higher rate of advanced PC disease when modelled as cumulative exposure of 6 months (OR 1.03, 95% CI 1.003-1.06, P = 0.031), and PC-specific death, when modeled as “ever vs. never” (OR 1.26, 95% CI 1.02-1.576, p = 0.031). Conclusions: Hydrophilic statins were associated with a clinically and statistically significant lower PC diagnosis and PC-specific death, while pantoprazole was associated with a higher rate of advanced PC disease and PC-specific death.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".