MP18-04 IMPACT OF PUTATIVE CHEMOPREVENTATIVE AGENTS ON PROSTATE CANCER DIAGNOSIS
Bibliographic record
Abstract
You have accessJournal of UrologyProstate Cancer: Detection & Screening II (MP18)1 Apr 2019MP18-04 IMPACT OF PUTATIVE CHEMOPREVENTATIVE AGENTS ON PROSTATE CANCER DIAGNOSIS Hanan Goldberg*, Faizan Mohsin, Zachary Klaassen, Thenappan Chandrasekar, Christopher Wallis, Jaime Omar Herrera Cáceres, Ardalan Ahmed, Dixon Woon, Shabbir Alibhai, Alejandro Berlin, Refik Saskin, Robert Hamilton, Girish Kulkarni, and Neil Fleshner Hanan Goldberg*Hanan Goldberg* More articles by this author , Faizan MohsinFaizan Mohsin More articles by this author , Zachary KlaassenZachary Klaassen More articles by this author , Thenappan ChandrasekarThenappan Chandrasekar More articles by this author , Christopher WallisChristopher Wallis More articles by this author , Jaime Omar Herrera CáceresJaime Omar Herrera Cáceres More articles by this author , Ardalan AhmedArdalan Ahmed More articles by this author , Dixon WoonDixon Woon More articles by this author , Shabbir AlibhaiShabbir Alibhai More articles by this author , Alejandro BerlinAlejandro Berlin More articles by this author , Refik SaskinRefik Saskin More articles by this author , Robert HamiltonRobert Hamilton More articles by this author , Girish KulkarniGirish Kulkarni More articles by this author , and Neil FleshnerNeil Fleshner More articles by this author View All Author Informationhttps://doi.org/10.1097/01.JU.0000555449.91087.2bAboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVES: Prostate cancer (PC) is the most common non-cutaneous cancer in Canadian men and the third most common cause of cancer death in males accounting for 10% of all male cancer deaths in Canada. Several observational and randomized studies have shown that use of commonly prescribed medications, including those used for the treatment of 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, such as proton pump inhibitors (PPI), on the rate of PC diagnosis, over more than 20 years of follow-up. METHODS: A retrospective population-based study using data from the Institute of clinical evaluative sciences (ICES), 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 analyzed medications included Statins (hydrophilic and hydrophobic), most commonly used diabetes drugs (metformin, insulins, sulfonylureas, and thiazolidinediones), PPIs, 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. 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 for PC diagnosis 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 (table 1). CONCLUSIONS: Hydrophilic statins with a clinically and statistically significant lower PC diagnosis. To our knowledge, this is the first study demonstrating a clear advantage of hydrophilic over hydrophobic statins in PC prevention. Source of Funding: This research was supported by the CUA CUOG Astellas Research Grant Program funded by Astellas Pharma Canada, Inc. and jointly established by Astellas Pharma Canada, Inc., CUOG, and the Canadian Urological Association. Toronto, Canada© 2019 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 201Issue Supplement 4April 2019Page: e264-e265 Advertisement Copyright & Permissions© 2019 by American Urological Association Education and Research, Inc.MetricsAuthor Information Hanan Goldberg* More articles by this author Faizan Mohsin More articles by this author Zachary Klaassen More articles by this author Thenappan Chandrasekar More articles by this author Christopher Wallis More articles by this author Jaime Omar Herrera Cáceres More articles by this author Ardalan Ahmed More articles by this author Dixon Woon More articles by this author Shabbir Alibhai More articles by this author Alejandro Berlin More articles by this author Refik Saskin More articles by this author Robert Hamilton More articles by this author Girish Kulkarni More articles by this author Neil Fleshner More articles by this author Expand All Advertisement PDF downloadLoading ...
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.194 | 0.041 |
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".