1146-P: SGLT2 Inhibitors and the Risk of Early Bladder Cancer Events
Bibliographic record
Abstract
SGLT2 inhibitors (SGLT2is) have been shown to reduce the risk of cardiovascular and mortality events in large cardiovascular outcome trials in patients with type 2 diabetes. However, their safety profile is controversial, with some trials reporting imbalances in bladder cancer events, all of which occurred relatively soon after randomization. To address this safety concern, we used 3 US healthcare claims databases and 1 UK primary care database (01/2013-12/2020) . In each database, we identified adults newly treated with either SGLT2is (total n=453,726) or GLP-1 receptor agonists (GLP1RAs, n=375,990) (comparison #1) , and SGLT2is (n=347,055) or DPP-4 inhibitors (DPP4is, n=854,141) (comparison #2) . Propensity score-based fine stratification was used to reweigh the cohorts, adjusting for a wide range of patient characteristics at baseline. Hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated for bladder cancer in each database using Cox proportional hazards models. After a median follow-up of 1.8 years, use of SGLT2is may slightly reduce the risk of bladder cancer, compared to GLP1RAs [HR (95% CI) =0.90 (0.81-1.00) ]. Compared to use of DPP4is, SGLT2is are not associated with bladder cancer incidence [HR (95% CI) : 1.00 (0.91-1.11) ] after a median follow-up of 2.0 years (Table) . Overall, the results of this large international cohort study provide reassurance on the short-term effects of SGLT2is on bladder cancer incidence. Disclosure D.Abrahami: None. H.Tesfaye: None. H.Yin: None. O.Yu: None. R.W.Platt: Consultant; Amgen Inc., Biogen, Merck & Co., Inc., Nant Pharma, Pfizer Inc. S.Schneeweiss: None. E.Patorno: Research Support; Boehringer Ingelheim International GmbH, National Institutes of Health, Patient-Centered Outcomes Research Institute. L.Azoulay: Consultant; Pfizer Inc. Funding Canadian Institutes of Health Research (PJT-169040)
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".