4881Empagliflozin reduces mortality in analyses adjusted for control of blood pressure, low density lipoprotein cholesterol and HbA1c over time
Bibliographic record
Abstract
Background: In EMPA-REG OUTCOME, empagliflozin given in addition to standard of care significantly reduced the risk of cardiovascular (CV) (HR 0.62 [95% CI 0.49, 0.77]) and all-cause (0.68 [0.57, 0.82]) mortality vs placebo in patients with type 2 diabetes and established CV disease. We investigated the effects of controlling blood pressure (BP), low density lipoprotein cholesterol (LDL-C) and HbA1c on the treatment difference in mortality. Methods: Patients were randomised to empagliflozin 10 mg, empagliflozin 25 mg, or placebo. CV and all-cause mortality were assessed in the pooled empagliflozin group vs placebo adjusting for control of BP, LDL-C and HbA1c at baseline and during the study as time-dependent covariates. Control was defined as systolic BP <140 mmHg and diastolic BP <90 mmHg, LDL-C <100 mg/dL, and HbA1c <7.5%. Results: Adjusting for control of BP, LDL-C, HbA1c and all these covariates at baseline and during the study, HRs for CV death with empagliflozin vs placebo were 0.61 (0.49, 0.76), 0.59 (0.47, 0.75), 0.62 (0.49, 0.78) and 0.61 (0.48, 0.76), and for all-cause mortality were 0.67 (0.56, 0.81), 0.66 (0.55, 0.79), 0.67 (0.56, 0.81) and 0.67 (0.56, 0.81), respectively (Figure).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".