Glucagon-like peptide-1 receptor agonists and the risk of major adverse cardiovascular events in patients with chronic kidney disease
Bibliographic record
Abstract
This thesis describes a population-based cohort study that characterizes the real-world effectiveness of glucagon-like peptide-1 receptor agonists (GLP1RA) in individuals with chronic kidney disease in Ontario. This population-based retrospective cohort study compared 24,576 new users of GLP1RA to 44,367 new users of dipeptidyl peptidase-4 inhibitors (DPP-4i), all of whom had eGFR <90ml/min/1.73m2. The primary outcome was major adverse cardiovascular events (MACE), comprising non-fatal myocardial infarction, unstable angina, non-fatal ischemic stroke or transient ischemic attack, coronary revascularization, and cardiovascular death. MACE occurred among 1296 (31.6 per 1000 person-years) GLP1RA users vs. 1374 (36.5 per 1000 person-years) DPP-4i users (sHR 0.88, 95% CI 0.80 to 0.97). The reduction in MACE was largely driven by a lower risk of cardiovascular death among GLP1RA users (sHR 0.72, 95% CI 0.62 to 0.85). In conclusion, in a population-based study of individuals with CKD, GLP1RA initiation was associated with a reduction in MACE.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".