Applying <scp>REWIND</scp> cardiovascular disease criteria to <scp>SUSTAIN</scp> 6 and <scp>PIONEER</scp> 6: An exploratory analysis of cardiovascular outcomes with semaglutide
Bibliographic record
Abstract
In the REWIND trial, dulaglutide reduced cardiovascular (CV) risk versus placebo in patients with type 2 diabetes in both the "established CV disease" (CVD) and "CV risk factor" subgroups. The SUSTAIN 6 and PIONEER 6 trials of semaglutide used different criteria for established CVD from those used in REWIND. The present post hoc analysis assessed the effect of semaglutide on major adverse CV events (MACE) in a pooled population of SUSTAIN 6 and PIONEER 6 patients, re-categorized into CV risk subgroups using the REWIND CVD criteria. In the pooled analysis (n = 6480), a lower percentage of patients were in the established CVD subgroup, when using the REWIND CVD criteria, compared with the original trial CVD criteria (66.5% vs. 83.8%, respectively). After re-categorization, the risk of MACE was significantly lower with semaglutide versus placebo in the established CVD subgroup (hazard ratio [HR] 0.74, 95% confidence interval [CI] 0.59, 0.92) and nonsignificantly lower in the CV risk factor subgroup (HR 0.84, 95% CI 0.55, 1.28) (P-interaction = 0.60). These results suggest that the CV effects of semaglutide may extend to patients with type 2 diabetes across the CV risk continuum.
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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 | 0.016 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.012 |
| 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) | 0.002 | 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".