Sodium‐glucose co‐transporter‐2 inhibitors and the risk of urosepsis: A multi‐site, prevalent new‐user cohort study
Bibliographic record
Abstract
AIM: To compare urosepsis rates in patients with type 2 diabetes treated using sodium-glucose co-transporter-2 inhibitors (SGLT2i) with dipeptidyl peptidase-4 inhibitors (DPP4i) in a real-world setting. METHODS: We conducted a matched cohort study using a prevalent new-user design with time-conditional propensity scores. New users of SGLT2i from seven Canadian provinces and the UK were matched to DPP4i users. The primary outcome was hospitalization with a diagnosis of urosepsis and the secondary outcome was Fournier's gangrene. Site-specific hazard ratios for urosepsis comparing SGLT2i with DPP4i were estimated using Cox proportional hazards models and pooled using a random effects meta-analysis. RESULTS: We included 208 244 users of SGLT2i and 208 244 users of DPP4i. Among SGLT2i users, 42% initiated canagliflozin, 31% dapagliflozin and 27% empagliflozin. During a mean follow-up of 0.9 years, patients initiating SGLT2i had a lower rate of urosepsis compared with those receiving DPP4i. The pooled adjusted hazard ratio was 0.58 (95% confidence interval [CI]: 0.42-0.80). The incidence rates of Fournier's gangrene were numerically similar in SGLT2i (0.08 per 1000 person-years; 95% CI: 0.05-0.13) and DPP4i users (0.14; 95% CI: 0.09-0.21). CONCLUSIONS: In this large, multi-site study, we did not observe an increased risk for urosepsis associated with SGLT2i compared with DPP4i among patients with type 2 diabetes in a real-world setting.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".