Baseline Characteristics of Patients Enrolled in the EMPACT-MI Trial
Bibliographic record
Abstract
AIMS: Empagliflozin has been shown to reduce the risk of adverse cardiovascular outcomes in patients with type 2 diabetes and in those with heart failure. The impact of empagliflozin in post-acute myocardial infarction (AMI) patients is unknown. METHODS AND RESULTS: The Study to Test the Effect of Empagliflozin on Hospitalization for Heart Failure and Mortality in Patients with Acute Myocardial Infarction (EMPACT-MI) trial screened 6610 participants with AMI and randomized 6522 to empagliflozin or placebo in addition to standard of care. The median (interquartile) age was 64 (56-71) years and 75.1% of patients were male. Major comorbidities included hypertension (69.1%), type 2 diabetes (31.7%), prior myocardial infarction (13.0%), and atrial fibrillation (10.9%). The majority (74.3%) of patients presented with an ST-elevation myocardial infarction. Overall, 56.9% of patients had acute signs or symptoms of congestion requiring treatment and 78.3% had left ventricular systolic dysfunction with ejection fraction <45%. Clinical characteristics, including baseline demographics, rates of revascularization, and cardiovascular medications at discharge were largely comparable to recent trials of the post-AMI population. CONCLUSION: The EMPACT-MI trial will establish the benefit and risks of empagliflozin treatment in patients with AMI.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".