Consistent effects of empagliflozin on cardiovascular and kidney outcomes irrespective of diabetic kidney disease categories: Insights from the <scp>EMPA‐REG OUTCOME</scp> trial
Bibliographic record
Abstract
AIM: To explore the cardiovascular (CV) and kidney effects of empagliflozin in patients with different clinical phenotypes of diabetic kidney disease (DKD) (i.e. with the presence or absence of overt albuminuria) participating in the EMPA-REG OUTCOME trial. MATERIALS AND METHODS: without overt albuminuria; n = 4893). Analyses included CV (death, hospitalization for heart failure, all-cause hospitalization) and selected kidney outcomes, change in eGFR and kidney safety. Cox proportional hazards models assessed the consistency of treatment effect across subgroups. RESULTS: Empagliflozin significantly reduced the risk of CV and kidney outcomes across all subgroups (P-values for interaction >.05), consistent with the overall trial population findings. Empagliflozin also significantly reduced the yearly loss of eGFR, assessed by chronic slopes, in all subgroups. The adverse event profile of empagliflozin was similar across all subgroups. CONCLUSIONS: Empagliflozin may improve CV and kidney outcomes and slow the progression of kidney disease in type 2 diabetes patients with DKD, irrespective of its clinical form, both with or without the presence of overt albuminuria.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".