Real-World Effectiveness Analysis of Switching From Liraglutide or Dulaglutide to Semaglutide in Patients With Type 2 Diabetes Mellitus: The Retrospective REALISE-DM Study
Why this work is in the frame
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Bibliographic record
Abstract
Injectable semaglutide is a glucagon-like peptide-1 receptor agonist (GLP-1 RA) that was previously shown to be superior to liraglutide and dulaglutide in head-to-head comparisons in GLP-1 RA-naïve individuals. It is hypothesized that semaglutide will cause further reductions in glycated hemoglobin A1c (HbA1c) and weight in type 2 diabetes mellitus (T2DM) patients previously treated with liraglutide or dulaglutide. The REALISE-DM study provides the first real-world evidence of the effectiveness and tolerability of semaglutide in patients switching from another GLP-1 RA. This retrospective real-world effectiveness analysis included T2DM adults who were on a stable dose of liraglutide or dulaglutide prior to switching to semaglutide. The primary outcome was change in HbA1c. Secondary outcomes were the changes in weight and body mass index (BMI), the occurrence of gastrointestinal side effects (GSEs), and discontinuations. Linear mixed models were used to estimate changes in HbA1c, weight, and BMI, and logistic regression was employed to analyze GSEs and discontinuations. Six months after the 164 patients in this study had switched to semaglutide, their mean HbA1c had decreased by 0.65% (7.1 mmol/mol) (95% prediction interval [PI]: 0.48, 0.81% [5.2, 8.9 mmol/mol]) from a baseline of 7.9% (interquartile range [IQR]: 7.3, 8.8) (62.8 mmol/mol [IQR: 56.3, 72.7]), while their weight and BMI had reduced by 1.69 kg (95% PI: 1.01, 2.37) and 0.59 kg/m2 (95% PI: 0.34, 0.84), respectively. Nineteen patients (11.6%) developed GSEs after switching. This study supports switching T2DM patients on liraglutide or dulaglutide to injectable semaglutide to achieve further reductions in HbA1c and weight. Although a small number of GSEs occurred, semaglutide was well tolerated by the majority of the patients.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it