Cardiorenal outcomes with ertugliflozin assessed according to baseline glucose‐lowering agent: An analysis from <scp>VERTIS CV</scp>
Bibliographic record
Abstract
Abstract Aim To assess selected cardiorenal outcomes with ertugliflozin according to use of baseline glucose‐lowering agent. Materials and Methods VERTIS CV was a cardiovascular (CV) outcome trial for ertugliflozin versus placebo, conducted in patients with type 2 diabetes and established atherosclerotic CV disease. The primary outcome was time to the first event of CV death, myocardial infarction or stroke (major adverse CV events [MACE]), with other CV outcomes also assessed. Outcomes were analysed using Cox proportional hazards models stratified by baseline use of metformin, insulin, sulphonylureas (SUs) and dipeptidyl peptidase‐4 (DPP‐4) inhibitors, with interaction testing to assess for treatment effect modification. Changes from baseline in glycaemic, metabolic and haemodynamic variables were also assessed. Results Of 8246 randomized patients, at baseline 6286 (76%) were on metformin, 3898 (47%) were on insulin, 3383 (41%) were on SUs and 911 (11%) were on DPP‐4 inhibitors, alone or in combination therapy (67% used >1 glucose‐lowering agent at baseline). For each glucose‐lowering agent evaluated, no evidence for effect modification was observed for MACE by baseline use of metformin (with: hazard ratio [HR] 0.92, 95% confidence interval [CI] 0.790, 1.073; without: 1.13, 95% CI 0.867, 1.480), insulin (with: HR 0.91, 95% CI 0.765, 1.092; without: 1.06, 95% CI 0.867, 1.293), SUs (with: HR 1.11, 95% CI 0.890, 1.388; without: 0.90, 95% CI 0.761, 1.060) or DPP‐4 inhibitors (with: HR 0.77, 95% CI 0.502, 1.173; without: 1.00, 95% CI 0.867, 1.147) (all P interaction > 0.05). Similar results were observed for all secondary outcomes analysed. Conclusion In VERTIS CV, the effects of ertugliflozin on cardiorenal outcomes were consistent across subgroups of patients stratified by baseline glucose‐lowering agent. ClinicalTrials.gov identifier: NCT01986881
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".