Long‐term weight loss and cardiorenal outcomes by baseline <scp>BMI</scp> in the <scp>VERTIS CV</scp> trial
Bibliographic record
Abstract
Abstract Aim To assess weight loss and cardiorenal outcomes by baseline body mass index (BMI) in VERTIS CV. Methods Patients with type 2 diabetes and atherosclerotic cardiovascular (CV) disease were randomized to ertugliflozin or placebo. These post hoc analyses evaluated cardiometabolic and cardiorenal outcomes (a composite of death from CV causes or hospitalization for heart failure [HHF], CV death, HHF and an exploratory composite kidney outcome including ≥40% estimated glomerular filtration rate [eGFR] decrease) by baseline BMI, using conventional clinical categories and Cox proportional hazards models. Results In total, 8246 adults were randomized (mean age 64.4 years, diabetes duration 13.0 years, BMI 32.0 kg/m 2 , 61% with BMI >30 kg/m 2 ). Absolute body weight reduction was greater with ertugliflozin versus placebo at 3 and 5 years in the overall population ( p < 0.001) and across BMI subgroups. Ertugliflozin increased the proportion of participants achieving ≥5% and ≥10% body weight reduction (ertugliflozin 34.9% and 13.6%, placebo 19.4% and 4.1%; odds ratio [95% confident interval, CI], 2.21 [1.76–2.77] and 3.65 [2.39–5.57], respectively) at 5 years. No significant difference was observed in the effect of ertugliflozin on HHF across BMI subgroups ( P interaction = 0.61). Similarly, no significant difference was observed in the effect of ertugliflozin on the kidney composite outcome across BMI subgroups ( P interaction = 0.39). Results were similar for other CV outcomes, and safety was consistent with the known ertugliflozin profile. Conclusion Weight loss was observed across baseline BMI and was sustained over 5 years of follow‐up. The effects of ertugliflozin on HHF and kidney composite were consistent across baseline BMI.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".