A pre-specified analysis of the Dapagliflozin and Prevention of Adverse Outcomes in Chronic Kidney Disease (DAPA-CKD) randomized controlled trial on the incidence of abrupt declines in kidney function
Bibliographic record
Abstract
This pre-specified analysis of DAPA-CKD assessed the impact of sodium-glucose cotransporter 2 inhibition on abrupt declines in kidney function in high-risk patients based on having chronic kidney disease (CKD) and substantial albuminuria. DAPA-CKD was a randomized, double-blind, placebo-controlled trial that had a median follow-up of 2.4 years. Adults with CKD (urinary albumin-to-creatinine ratio 200-5000 mg/g and estimated glomerular filtration rate 25-75 mL/min/1.73m2) were randomized to dapagliflozin 10 mg/day matched to placebo (2152 individuals each). An abrupt decline in kidney function was defined as a pre-specified endpoint of doubling of serum creatinine between two subsequent study visits. We also assessed a post-hoc analysis of investigator-reported acute kidney injury-related serious adverse events. Doubling of serum creatinine between two subsequent visits (median time-interval 100 days) occurred in 63 (2.9%) and 91 (4.2%) participants in the dapagliflozin and placebo groups, respectively (hazard ratio 0.68 [95% confidence interval 0.49, 0.94]). Accounting for the competing risk of mortality did not alter our findings. There was no heterogeneity in the effect of dapagliflozin on abrupt declines in kidney function based on baseline subgroups. Acute kidney injury-related serious adverse events were not significantly different and occurred in 52 (2.5%) and 69 (3.2%) participants in the dapagliflozin and placebo groups, respectively (0.77 [0.54, 1.10]). Thus, in patients with CKD and substantial albuminuria, dapagliflozin reduced the risk of abrupt declines in 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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.009 |
| 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 | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".