Real-world use of once-weekly semaglutide in diverse patient populations with type 2 diabetes: pooled analysis of four SURE studies
Bibliographic record
Abstract
Background and aims Observational studies reflect real-world use of a medication in diverse patient populations and provide evidence on outcomes in routine clinical practice. Results from the first four individual SURE studies (Canada, Denmark/Sweden, Switzerland and UK) investigating real-world use of semaglutide consistently showed significant HbA1c and body weight reductions. Materials and methods Data from populations of patients enrolled in the studies were pooled for this analysis. Semaglutide and other anti-hyperglycaemic drugs were prescribed at the physician’s discretion. Change from baseline (BL) to end of study (EOS; ~30 weeks) in HbA1c and body weight are reported in the overall population, and the following BL subgroups: glucagon-like peptide-1 receptor agonist (GLP-1RA) experience (switcher/naïve); dipeptidyl peptidase-4 inhibitor (DPP-4i) status; HbA1c; BMI ; age and diabetes duration. Results Overall, 1,212 patients were included in the full analysis set, with BL characteristics reflective of real-world practice. Significant HbA1c (p<0.0001) and body weight (p<0.01) reductions were observed with semaglutide in the overall population and all subgroups. The greatest HbA1c reduction was observed with BL HbA1c > 9%, and the smallest with BL HbA1c < 7%; the greatest body weight reductions were observed with BL BMI ≥ 35 kg/m2 or in DPP-4i switchers, and the smallest with BL BMI < 25 kg/m2. Conclusion In a pooled analysis of real-world data, patients with type 2 diabetes initiating semaglutide experienced significant reductions in HbA1c and body weight, in the overall population and in subgroups based on various BL characteristics, including switching from another GLP-1RA. Publication History Article published online: 26 May 2022 © 2022. Thieme. All rights reserved. Georg Thieme Verlag KG Rüdigerstraße 14, 70469 Stuttgart, Germany
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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 | 0.027 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.017 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".