Comparative cardiovascular and hypoglycaemic safety of glimepiride in type 2 diabetes: A population‐based cohort study
Bibliographic record
Abstract
AIM: To assess the incidence of cardiovascular and hypoglycaemic adverse events associated with glimepiride compared with other second-generation sulphonylureas among patients with type 2 diabetes in a real-world clinical setting. MATERIALS AND METHODS: We identified all sulphonylurea initiators between 1998 and 2017 in the UK Clinical Practice Research Datalink. Using a prevalent new-user design, glimepiride initiators were matched 1:4 with initiators of other second-generation sulphonylureas on calendar time, prior sulphonylurea use, and time-conditional high-dimensional propensity score. Cox proportional hazards models yielded adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) for myocardial infarction, ischaemic stroke, severe hypoglycaemia, cardiovascular death, and all-cause mortality. RESULTS: Among 66 032 sulphonylurea new users, 6438 initiated glimepiride and were matched to up to 20 582 initiators of other second-generation sulphonylureas. During a mean follow-up of 1.3 years, glimepiride was associated with a similar incidence of myocardial infarction (HR 0.99, 95% CI 0.75-1.30) and ischaemic stroke (HR 0.96, 95% CI 0.72-1.27) compared with other second-generation sulphonylureas, while there was a non-significant trend towards a higher incidence of severe hypoglycaemia (HR 1.24, 95% CI 0.92-1.68). Glimepiride was also associated with a lower incidence of all-cause mortality (HR 0.77, 95% CI 0.67-0.89), and a non-significant but similar trend for cardiovascular death (HR 0.83, 95% CI 0.65-1.05). CONCLUSIONS: Glimepiride was associated with a lower incidence of all-cause mortality compared with other second-generation sulphonylureas.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".