Semaglutide Effects on Cardiovascular Outcomes in People With Overweight or Obesity (SELECT) rationale and design
Bibliographic record
Abstract
Cardiovascular disease (CVD) is a major cause of morbidity and mortality. Although it has been widely appreciated that obesity is a major risk factor for CVD, treatments that produce effective, durable weight loss and the impact of weight reduction in reducing cardiovascular risk have been elusive. Instead, progress in CVD risk reduction has been achieved through medications indicated for controlling lipids, hyperglycemia, blood pressure, heart failure, inflammation, and/or thrombosis. Obesity has been implicated as promoting all these issues, suggesting that sustained, effective weight loss may have independent cardiovascular benefit. GLP-1 receptor agonists (RAs) reduce weight, improve glycemia, decrease cardiovascular events in those with diabetes, and may have additional cardioprotective effects. The GLP-1 RA semaglutide is in phase 3 studies as a medication for obesity treatment at a dose of 2.4 mg subcutaneously (s.c.) once weekly. Semaglutide Effects on Heart Disease and Stroke in Patients with Overweight or Obesity (SELECT) is a randomized, double-blind, parallel-group trial testing if semaglutide 2.4 mg subcutaneously once weekly is superior to placebo when added to standard of care for preventing major adverse cardiovascular events in patients with established CVD and overweight or obesity but without diabetes. SELECT is the first cardiovascular outcomes trial to evaluate superiority in major adverse cardiovascular events reduction for an antiobesity medication in such a population. As such, SELECT has the potential for advancing new approaches to CVD risk reduction while targeting obesity.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".