PD52-07 MEDICATION USE AND KIDNEY CANCER RISK: A POPULATION-BASED STUDY
Notice bibliographique
Résumé
You have accessJournal of UrologyKidney Cancer: Epidemiology & Evaluation/Staging III1 Apr 2017PD52-07 MEDICATION USE AND KIDNEY CANCER RISK: A POPULATION-BASED STUDY Madhur Nayan, David Juurlink, Peter Austin, Erin Macdonald, Antonio Finelli, Girish Kulkarni, and Robert Hamilton Madhur NayanMadhur Nayan More articles by this author , David JuurlinkDavid Juurlink More articles by this author , Peter AustinPeter Austin More articles by this author , Erin MacdonaldErin Macdonald More articles by this author , Antonio FinelliAntonio Finelli More articles by this author , Girish KulkarniGirish Kulkarni More articles by this author , and Robert HamiltonRobert Hamilton More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2017.02.2195AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES Exposure to commonly-prescribed medications may be associated with cancer risk. However, there is limited data in kidney cancer. Furthermore, methods of classifying cumulative medication exposure in previous studies may be prone to bias. METHODS We conducted a population-based case-control study utilizing health care databases in Ontario, Canada. Individuals enrolled as cases were aged ≥66 with an incident diagnosis of kidney cancer. For each individual enrolled as a case, we identified up to four individuals without kidney cancer as controls matched on age, sex, history of hypertension, comorbidity score, and geographic location. Cumulative exposure to commonly prescribed medications hypothesized to modulate cancer risk were obtained using prescription claims data. We modelled exposure in four different fashions: 1) as continuous exposures using a) fractional polynomials (which allow for non-linear relationship between a continuous exposure and outcome) or b) a linear relationship; and 2) as dichotomous exposures denoting a) 3 years or greater vs. less than 3 years of cumulative exposure; or b) ′ever′ vs. ′never′ exposure. We used conditional logistic regression to estimate the association of medication exposure on incident kidney cancer. RESULTS We identified 10,377 incident cases of kidney cancer and 35,939 matched controls. When utilizing fractional polynomials, increasing cumulative exposure to aspirin, selective serotonin reuptake inhibitors, and proton-pump inhibitors were associated with significantly reduced risk of developing kidney cancer, while increasing exposure to anti-hypertensive drugs was associated with significantly increased risk (Table 1). The directions of association were relatively consistent across analyses; however, the magnitudes were sensitive to the method of analysis (Table 2). CONCLUSIONS Our study provides impetus to further explore the effect of commonly-prescribed medications on carcinogenesis to identify modifiable pharmacological interventions to reduce the risk of kidney cancer. © 2017FiguresReferencesRelatedDetails Volume 197Issue 4SApril 2017Page: e991-e992 Advertisement Copyright & Permissions© 2017MetricsAuthor Information Madhur Nayan More articles by this author David Juurlink More articles by this author Peter Austin More articles by this author Erin Macdonald More articles by this author Antonio Finelli More articles by this author Girish Kulkarni More articles by this author Robert Hamilton More articles by this author Expand All Advertisement 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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».