The Real-World Observational Prospective Study of Health Outcomes with Dulaglutide & Liraglutide in Type 2 Diabetes Patients (TROPHIES): final 24-month primary endpoint analysis
Bibliographic record
Abstract
Question How long do people with Type 2 Diabetes (T2D) remain on their first glucagon-like peptide-1 receptor agonists (GLP-1 RA) without a significant treatment change? Methodology TROPHIES was a 24-month, prospective, non-comparative, observational study in adult patients with T2D initiating their first injectable glucose-lowering treatment with once-weekly dulaglutide (DU; N=1,014) or once-daily liraglutide (LIRA; N=991) in France, Germany, and Italy. Primary objective: to assess the time patients remained on their first GLP-1 RA without a significant treatment change due to treatment- or diabetes-related factors. Results Kaplan-Meier (KM) probability (95% CI) of no significant treatment change at 24 months was 0.71 (0.68–0.74) and 0.53 (0.49–0.56) in the DU and LIRA cohorts, respectively Two-hundred and eighty-six (28.2%) and 448 (45.2%) patients receiving DU and LIRA, respectively, had a significant treatment change. The main driver of treatment change in the DU and LIRA cohorts was intensification with an add-on therapy (insulin or OAD) and intensification with dose increase of GLP-1 RA, respectively. KM probability (95% CI) of GLP-1 RA persistence at 24 months was high in both cohorts: DU 0.82 (0.80–0.85); LIRA 0.75 (0.72–0.78). Conclusion In summary, the probabilities of no significant treatment change over 24 months were estimated as higher in the DU cohort than in the LIRA cohort in this non-comparative analysis, with good persistence in both cohorts. Publication History Article published online: 02 May 2023 © 2023. Thieme. All rights reserved. Georg Thieme Verlag 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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".