Review: Sulphonylureas and cardiovascular risk: facts and controversies
Bibliographic record
Abstract
Cardiovascular complications are the principal cause of death in type 2 diabetes. The importance of glycaemic control in preventing cardiovascular complications has been demonstrated. However, some oral antidiabetic agents and especially some sulphonylureas (SU) have been accused of having a deleterious effect on cardiovascular risk. A retrospective analysis of the administrative database of Saskatchewan Health for 5,795 subjects, identified by their first-ever dispensation for an oral antidiabetic agent, suggests that a higher exposure to SUs was associated with increased mortality. Nevertheless, the effects of SUs on cardiac ATP-sensitive potassium channels in experimental studies vary between agents and studies, so that the clinical relevance of this phenomenon is unclear. Moreover, 11 years of follow-up of patients randomised to glibenclamide or chlorpropamide in the United Kingdom Prospective Diabetes Study demonstrated no adverse effects on a range of cardiovascular end points. Despite SU structural differences and differences in binding to cardiac SU receptors, the clinical evidence base does not support the selection of one sulphonylurea over another on the basis of ischaemic preconditioning, possibly because ischaemic preconditioning may be blunted or absent in diabetes. The main objective remains the prevention or delay of diabetic complications through improvement of glycaemic control together with other cardiovascular risk factors.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".