Kidney protection with canagliflozin: A combined analysis of the randomized <scp>CANVAS</scp> program and <scp>CREDENCE</scp> trials
Bibliographic record
Abstract
Abstract Aim In the CANVAS Program and CREDENCE trials, the sodium glucose co‐transporter 2 inhibitor canagliflozin reduced the risk of cardiovascular and kidney events in patients with type 2 diabetes. The current study analysed a pooled population to ascertain the kidney protection provided by canagliflozin across the full spectrum of kidney parameters. Methods This post‐hoc pooled analysis of the CANVAS Program (N = 10 142) and CREDENCE trial (N = 4401), assessed the risk of the primary kidney composite (doubling of serum creatinine, end‐stage kidney disease, renal death), in all patients and subgroups defined by baseline estimated glomerular filtration rate (<30, 30 to <45, 45 to <60 and ≥60 ml/min/1.73 m 2 ), albuminuria [<30, 30‐300, >300 mg/g (<3.39, 3.39‐33.9, >33.9 mg/mmol)] and 2012 Kidney Disease: Improving Global Outcomes (KDIGO) classification of chronic kidney disease (low/moderate, high and very high risk). Results In the overall population, the risk for the primary kidney composite outcome was 37% lower in the canagliflozin group versus placebo (HR: 0.63; 95% CI: 0.53, 0.77; p < .001). There was no evidence of heterogeneity in the kidney protective effects of canagliflozin across a range of kidney risks when stratified by baseline estimated glomerular filtration rate, albuminuria or KDIGO risk category (all p interaction > .05). A statistically significant risk reduction of the primary kidney composite outcome was sustained by approximately 18 months after randomization. Conclusions These results emphasize a critical role of canagliflozin in kidney protection across a broad spectrum of participants with type 2 diabetes with varying levels of kidney function.
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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 | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.017 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".