Efficacy and Safety of Dapagliflozin by Baseline Glycemic Status: A Prespecified Analysis From the DAPA-CKD Trial
Bibliographic record
Abstract
OBJECTIVE The Dapagliflozin and Prevention of Adverse outcomes in Chronic Kidney Disease (DAPA-CKD) study demonstrated risk reduction for kidney and cardiovascular outcomes with dapagliflozin versus placebo in participants with chronic kidney disease (CKD) with and without diabetes. We compared outcomes according to baseline glycemic status. RESEARCH DESIGN AND METHODS We enrolled participants with CKD, estimated glomerular filtration rate (eGFR) 25–75 mL/min/1.73 m2, and urinary albumin-to-creatinine ratio 200–5,000 mg/g. The primary composite end point was sustained eGFR decline ≥50%, end-stage kidney disease, or kidney or cardiovascular death. RESULTS Of 4,304 participants, 738 had normoglycemia, 660 had prediabetes, and 2,906 had type 2 diabetes. The effect of dapagliflozin on the primary outcome was consistent (P for interaction = 0.19) in normoglycemia (hazard ratio [HR] 0.62 [95% CI 0.39, 1.01]), prediabetes (HR 0.37 [0.21, 0.66]), and type 2 diabetes (HR 0.64 [0.52, 0.79]). We found no evidence for effect modification on any outcome. Adverse events were similar, with no major hypoglycemia or ketoacidosis in participants with normoglycemia or prediabetes. CONCLUSIONS Dapagliflozin safely reduced kidney and cardiovascular events independent of baseline glycemic status.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".