Generalizability of glucagon‐like peptide‐1 receptor agonist cardiovascular outcome trials to the overall type 2 diabetes population in the United States
Bibliographic record
Abstract
AIM: To examine the generalizability of results from glucagon-like peptide-1 receptor agonist (GLP-1 RA) cardiovascular outcome trials (CVOTs) in the US type 2 diabetes (T2D) population. MATERIALS AND METHODS: Patients enrolled or eligible for inclusion in four CVOTs (EXSCEL, LEADER, REWIND, and SUSTAIN-6) were examined in reference to a retrospective clinical database weighted to match the age and sex distribution of the US adult T2D population. We descriptively compared key baseline characteristics of the populations enrolled in each trial to those of the reference population and estimated the proportions of individuals in the reference population represented by those in the trials for each characteristic. We also estimated the proportions of individuals in the reference population that might have been enrolled in each trial based upon meeting the trial inclusion and exclusion (I/E) criteria. RESULTS: No trial's enrolled population perfectly matched the reference population in key characteristics. The EXSCEL population most closely matched in mean age (62.7 vs. 60.5 years) and percentage with estimated glomerular filtration rate <60 (18.6 vs. 17.3%), while REWIND most closely matched in HbA1c, sex distribution, and proportion with a prior myocardial infarction. Based on I/E criteria, 42.6% of the reference population were eligible for enrolment in REWIND, versus 15.9% in EXSCEL, 13.0% in SUSTAIN-6, and 12.9% in LEADER. CONCLUSIONS: Although none of the trials are fully representative of the general population, among the four trials examined, results from baseline REWIND were found to be more generalizable to the US adult T2D population than those of other GLP-1 RA CVOTs.
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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 | 0.105 | 0.219 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".