Chronic prednisone, metformin, and nonsteroidal anti-inflammatory drug use and clinical outcome in a cohort of bladder cancer patients undergoing radical cystectomy in Québec, Canada
Bibliographic record
Abstract
BACKGROUND: Studies have suggested a positive association between bladder cancer (BC) outcome and comedication use, including nonsteroidal anti-inflammatory drugs (NSAID), metformin, and prednisone use. To validate these associations, we evaluated whether these medications were associated with clinical outcome in a Canadian cohort of BC patients. METHODS: This is a retrospective cohort study on BC patients undergoing radical cystectomy (RC) in Québec province in 2000-2015, as registered in the provincial health administration databases. Medication use was considered chronic when prescribed for ≥ 1 year. Overall (OS), disease-specific (DSS) and recurrence-free (RFS) survival were compared using multivariable Cox proportional hazards models. Covariates included age, Charlson's comorbidity index, region of residence, year of RC, distance to hospital, hospital type, hospital and surgeon annual RC volume, neoadjuvant chemotherapy use, and type of bladder diversion, as well as mutual adjustment for concomitant comedication use (statins, NSAIDs, metformin, and prednisone). RESULTS: Of 3742 patients included, 293, 420, and 1503 patients chronically used prednisone, metformin, and NSAIDs before surgery, respectively. In multivariable analyses, preoperative prednisone use was associated with improved OS (HR 0.67, 95%CI 0.55-0.82), DSS (HR 0.58, 95%CI 0.45-0.76), and RFS (HR 0.61, 95%CI 0.47-0.78). Patients who chronically used metformin preoperatively had a worse OS (HR 1.29, 95%CI 1.07-1.55), DSS (HR 1.38, 95%CI 1.10-1.72), and RFS (HR 1.41, 95%CI 1.13-1.74). Preoperative, chronic NSAID use was not significantly associated with all clinical outcomes, with adjusted HRs for OS, DSS, and RFS of 1.10 (95%CI 0.95-1.27), 1.24 (95%CI 1.03-1.48), and 1.22 (95%CI 1.03-1.45), respectively. Directionality of findings was similar when stratifying by comedication use in the year following surgery. Results were similar after propensity-score matching too. CONCLUSIONS: In our Canadian cohort of BC undergoing RC, chronic prednisone use was associated with improved clinical outcomes, while metformin and NSAID were not.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".