Comparative risk of infection of medications used for type 2 diabetes
Bibliographic record
Abstract
INTRODUCTION: Glucose-lowering drugs pose a potential infection risk among individuals with type 2 diabetes. The U.S. Food and Drug Administration has issued safety warnings regarding increased risks of urinary tract infections (UTIs) and genital infections with sodium-glucose cotransporter 2 (SGLT2) inhibitors. However, the infection risk associated with other glucose-lowering drugs remains unclear. We conducted a PubMed database search to review the infection risk of glucose-lowering drugs, focusing on meta-analysis of randomized controlled trials. AREAS COVERED: We described the infection risks associated with SGLT2 inhibitors, dipeptidyl peptidase-4 (DPP-4) inhibitors, glucose-like peptide-1 receptor agonists, metformin, and thiazolidinediones, covering infections of the genitourinary, respiratory, and gastrointestinal systems, including skin and soft tissue infections (SSTIs). EXPERT OPINION: SGLT2 inhibitors are associated with a higher genital infection risk, while their UTI risk remains inconclusive. DPP-4 inhibitors could be a treatment option for those intolerant to SGLT2 inhibitors, given their lower genital infection risk compared to placebo. Uncertainty persists regarding the risks of respiratory infections, gastroenteritis, and SSTIs with SGLT2 inhibitors. Limited evidence is available regarding the impact of DPP-4 inhibitors on respiratory infections. Additional research is needed to determine the comparative infection risk of other glucose-lowering drugs.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".