Positioning sulphonylureas in a modern treatment algorithm for patients with type 2 diabetes: Expert opinion from a European consensus panel
Bibliographic record
Abstract
The large number of pharmacological agents available to treat type 2 diabetes (T2D) makes choosing the optimal drug for any given patient a complex task. Because newer agents offer several advantages, whether and when sulphonylureas (SUs) should still be used to treat T2D is controversial. Published treatment guidelines and recommendations should govern the general approach to diabetes management. However, expert opinions can aid in better understanding local practices and in formulating individual choices. The current consensus paper aims to provide additional guidance on the use of SUs in T2D. We summarize current local treatment guidelines in European countries, showing that SUs are still widely proposed as second-line treatment after metformin and are often ranked at the same level as newer glucose-lowering medications. Strong evidence now shows that sodium-glucose co-transporter-2 inhibitors (SGLT-2is) and glucagon-like peptide-1 receptor agonists (GLP-1RAs) are associated with low hypoglycaemia risk, promote weight loss, and exert a positive impact on vascular, cardiac and renal endpoints. Thus, using SUs in place of SGLT-2is and GLP-1RAs may deprive patients of key advantages and potentially important cardiorenal benefits. In subjects with ascertained cardiovascular disease or at very high cardiovascular risk, SGLT-2is and/or GLP-1RAs should be used as part of diabetes management, in the absence of contraindications. Routine utilization of SUs as second-line agents continues to be acceptable in resource-constrained settings.
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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 | 0.028 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".