P5334Effect of empagliflozin on cardiovascular events including recurrent events in the EMPA-REG OUTCOME trial
Bibliographic record
Abstract
Background: In the EMPA-REG OUTCOME trial in patients with type 2 diabetes and established CV disease, empagliflozin reduced the risk of 3-point MACE (composite of CV death, MI, or stroke) by 14%, CV death by 38% and hospitalisation for heart failure (HHF) by 35% vs placebo in analyses of time to first event. We assessed the effect of empagliflozin on all (first and recurrent) CV events. Methods: Patients were randomised to receive empagliflozin 10 mg, empagliflozin 25 mg, or placebo in addition to standard of care. We assessed the effects of empagliflozin pooled vs placebo based on all adjudicated CV events using a negative binomial model with confidence intervals based on robust error variance estimators to account for within-subject correlation. Results: A total of 7020 patients were treated (mean [SD] age 63 [9] years, 71% male, 47% with history of MI, 23% with history of stroke, 10% with HF). In analyses including all events, the event rate ratio (95% CI) with empagliflozin vs placebo was 0.78 (0.67, 0.91; p=0.0020) for 3-point MACE, 0.79 (0.620, 0.998; p=0.0486) for MI, 1.10 (0.82, 1.49; p=0.5248) for stroke, 0.62 (0.49, 0.77) for CV death, 0.58 (0.42, 0.81; p=0.0012) for HHF, 0.56 (0.45, 0.69; p<0.0001) for the composite of CV death or HHF, and 0.80 (0.67, 0.95; p=0.0119) for the composite of MI or coronary revascularisation. Results were consistent with analyses of first events (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 | 0.002 | 0.002 |
| 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".