PD52-07 MEDICATION USE AND KIDNEY CANCER RISK: A POPULATION-BASED STUDY
Bibliographic record
Abstract
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 ...
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".