Medication use and kidney cancer survival: A population‐based study
Bibliographic record
Abstract
Several studies demonstrate that use of commonly prescribed medications is associated with improved survival in various malignancies. Methods of classifying medication use in many of these studies, however, do not account for intermittent or cumulative use. Moreover, there are limited data in kidney cancer. Therefore, we performed a population-based cohort study utilizing healthcare databases in Ontario, Canada. We identified patients aged ≥65 with an incident diagnosis of kidney cancer between 1997 and 2013 and examined use of nine putative anti-neoplastic medications using prescription claims. Cox proportional hazard models evaluated the association of medication exposure on cancer-specific and overall survival. We conducted three separate analyses: the effect of cumulative duration of exposure to the study medications on outcomes, the effect of current exposure (in a binary nature) and the effect of exposure at diagnosis. During the 16-year study period, we studied 9,124 patients. Increasing cumulative use of angiotensin-converting enzyme inhibitors, non-steroidal anti-inflammatory drugs (NSAIDs) and selective serotonin reuptake inhibitors were associated with markedly improved cancer-specific survival; increasing use of NSAIDs was associated with markedly improved overall survival. These results were generally discordant with analyses evaluating the effect of current use and exposure at diagnosis. In conclusion, pharmacoepidemiology studies may be sensitive to the method of analysis; cumulative use analyses may be the most robust as it accounts for intermittent use and supports a dose-outcome relationship. Prospective studies are needed to confirm whether patients diagnosed with kidney cancer should be started on an angiotensin-converting enzyme inhibitor, NSAID or selective serotonin reuptake inhibitor to improve survival.
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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 | 0.000 | 0.000 |
| 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".