Impact of oral hypoglycemic agents on mortality among diabetic patients with non-muscle-invasive bladder cancer: A population-based analysis
Bibliographic record
Abstract
INTRODUCTION: Non-muscle-invasive bladder cancer (NMIBC) accounts for 75-85% of all urothelial bladder cancers (UBC). Many UBC patients are also afflicted by diabetes mellitus (DM). It has been postulated that several oral hypoglycemic agents could impact disease-specific survival (DSS), but the data are sparse among NMIBC patients. Our primary objective was to evaluate the impact of metformin on DSS and overall survival (OS) in NMIBC patients. METHODS: This is a retrospective, population-based study that used linked administrative databases to identify diabetic patients ≥66 years who were subsequently diagnosed with NMIBC in Ontario between 1992 and 2012. Cumulative use of metformin and other hypoglycemic agent were calculated before and after NMIBC diagnosis. DSS and OS were estimated using multivariable competing risk and Cox proportional hazards models, respectively. RESULTS: A total of 1742 subjects were included in the study. After a median followup of 5.2 years, 1122 (64%) had died, including 247 (15%) deaths as a result of UBC. On multivariable analysis, cumulative duration of metformin use after NMIBC diagnosis did not appear to impact DSS (hazard ratio [HR] 1.1; 95% confidence interval [CI] 0.92-1.2), whereas glyburide use appeared to have a detrimental effect (HR 1.17; 95% CI 1.02-1.3). None of the other hypoglycemic agents had an impact on OS. CONCLUSIONS: In this large, population-based study, we have provided further evidence that metformin use does not significantly impact DSS among diabetic patients diagnosed with NMIBC. However, our findings demonstrate that glyburide use inversely affects DSS. The detrimental effect of glyburide on DSS will require further validation.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".