MP18-04 IMPACT OF PUTATIVE CHEMOPREVENTATIVE AGENTS ON PROSTATE CANCER DIAGNOSIS
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Résumé
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 ...
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,003 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,194 | 0,041 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».