Five comparative cohorts to assess the risk of genital tract infections associated with sodium‐glucose cotransporter‐2 inhibitors initiation in type 2 diabetes mellitus
Bibliographic record
Abstract
AIM: To assess the association between SGLT-2 inhibitors initiation and genital tract infections (GTIs) among patients with type 2 diabetes. METHODS: A population-based cohort study using administrative healthcare data from Alberta, Canada, and primary care data from the UK's Clinical Practice Research Datalink (CPRD). Among new metformin users, we identified new users of SGLT-2 inhibitors and five active comparator cohorts (new users of dipeptidyl peptidase-4 (DPP-4) inhibitors, sulfonylureas (SU), glucagon-like peptide-1 receptor agonists (GLP-1 RA), thiazolidinediones (TZD) and insulin). The outcome of interest was a composite GTI outcome. In each cohort, we used high-dimensional propensity score matching to adjust for confounding and conditional Cox proportional hazards regression to estimate the hazard ratios (HR). We used random-effects meta-analysis to combine aggregate data across databases. RESULTS: The risk of GTI was higher for SGLT-2 inhibitors users compared with DPP4inhibitor users (pooled HR 2.68, 95% CI 2.19 3.28), SU users (3.29, 2.62-4.13), GLP1-RA users (2.51, 1.90-3.31), TZD users (4.17, 2.46-7.08) and insulin users (1.86, 1.27-2.73). CONCLUSION: In five comparative cohorts, SGLT-2 inhibitors initiation is associated with a higher risk of GTIs. These findings from real-world data are consistent with placebo-controlled randomized controlled trials.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.007 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".