Glucagon-Like Peptide 1 Receptor Agonists, Carotid Atherosclerosis, and Cardiovascular Outcomes
Bibliographic record
Abstract
Glucagon-like peptide 1 receptor agonists (GLP-1 RA) represent an integral part of the arsenal used in clinical practice to improve cardiovascular outcomes in patients with diabetes. Large-scale randomized controlled trials have shown that liraglutide, dulaglutide, albiglutide, and semaglutide all reduce the risk of cardiovascular events in patients with diabetes and either established atherosclerotic cardiovascular disease (ASCVD) or high-risk characteristics (1–4). In contrast, exenatide and lixisenatide improved glycemic control but did not have a sizable impact on cardiovascular events in clinical trials (5,6), suggesting that glycemic control and cardiovascular outcomes are at least partly uncoupled. A meta-analysis including 42,920 participants from five randomized trials demonstrated that GLP-1 RA as a drug class are associated with a significant 13% reduction in the risk of the composite of myocardial infarction, stroke, or cardiovascular death in patients with diabetes and established ASCVD (7). GLP-1 RA are recommended in patients with diabetes and established ASCVD or at high risk of ASCVD (8). They are also indicated as second-line therapy in patients without ASCVD (or without indicators of high risk) who do not meet treatment goals with lifestyle modifications and metformin alone, and they are preferred to insulin if injectable therapy is needed (8). Although the …
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.013 | 0.009 |
| Insufficient payload (model declined to judge) | 0.013 | 0.006 |
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".