3910SUSTAIN 6: a post-hoc analysis of the effect of semaglutide on cardiovascular outcomes over time in subjects with type 2 diabetes
Bibliographic record
Abstract
Background: Semaglutide is a glucagon-like peptide-1 (GLP-1) analogue in development for type 2 diabetes (T2D). SUSTAIN 6, a 2-year, cardiovascular (CV) outcomes, randomised, placebo-controlled trial, was conducted in 3297 adults with T2D at high CV risk. Once-weekly subcutaneous semaglutide (0.5 or 1.0 mg) added to standard of care led to a significant 26% reduction (hazard ratio [HR], 0.74; 95% confidence interval [CI], 0.58–0.95) in risk of the primary outcome (CV death, non-fatal myocardial infarction (MI) or non-fatal stroke) vs placebo, driven by risk reductions in non-fatal MI (26%; HR, 0.74; 95% CI, 0.51–1.08) and non-fatal stroke (39%; HR, 0.61; 95% CI, 0.38–0.99). Purpose: To explore the effect of semaglutide on the primary outcome over time. This analysis can help support mechanistic understanding by evaluating whether there is evidence of a weakened, strengthened or constant treatment effect during the trial. Methods: An extended Cox model with a time-varying treatment effect was used to plot the cumulative treatment coefficient (logarithm of the HR) in order to further assess the dynamic in the overall treatment effect. These results were compared to the cumulative incidence over time of subjects with a CV event for each treatment, as depicted in a Kaplan–Meier plot.
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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 | 0.018 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".