Geographic and racial variability in kidney, cardiovascular and safety outcomes with canagliflozin: A secondary analysis of the <scp>CREDENCE</scp> randomized trial
Bibliographic record
Abstract
AIM: To explore the effect of canagliflozin on kidney and cardiovascular events and safety outcomes in individuals with type 2 diabetes and chronic kidney disease across geographic regions and racial groups. MATERIALS AND METHODS: A stratified Cox proportional hazards model was used to assess efficacy and safety outcomes by geographic region and racial group. The primary composite outcome was a composite of end-stage kidney disease (ESKD), doubling of the serum creatinine (SCr) level, or death from kidney or cardiovascular causes. Secondary outcomes included: (i) cardiovascular death or heart failure (HF) hospitalization; (ii) cardiovascular death, myocardial infarction (MI) or stroke; (iii) HF hospitalization; (iv) doubling of the SCr level, ESKD or kidney death; (v) cardiovascular death; (vi) all-cause death; and (vii) cardiovascular death, MI, stroke, or hospitalization for HF or for unstable angina. RESULTS: The 4401 patients were divided into six geographic region subgroups: North America (n = 1182, 27%), Central and South America (n = 941, 21%), Eastern Europe (n = 947, 21%), Western Europe (n = 421, 10%), Asia (n = 749, 17%) and Other (n = 161, 4%). The analyses included four racial groups: White (n = 2931, 67%), Black or African American (n = 224, 5%), Asian (n = 877, 20%) and Other (n = 369, 8%). Canagliflozin reduced the relative risk of the primary composite outcome in the overall trial by 30% (hazard ratio 0.70, 95% confidence interval 0.59-0.82; P = 0.00001). Across geographic regions and racial groups, canagliflozin consistently reduced the primary composite endpoint without evidence of heterogeneity (interaction P values of 0.39 and 0.91, respectively) or significant safety outcome differences. CONCLUSIONS: Canagliflozin reduces the risk of kidney and cardiovascular events similarly across geographic regions and racial groups.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| 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 | 0.001 |
| 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".