Ischemia Management with Accupril post bypass Graft via Inhibition of angiotensin coNverting enzyme (IMAGINE): a multicentre randomized trial - design and rationale.
Bibliographic record
Abstract
BACKGROUND: Coronary artery bypass grafting (CABG) remains the revascularization treatment of choice for patients with severely symptomatic or life-threatening coronary artery disease (CAD). However, 9% to 25% of the patients undergoing CABG will suffer a recurrent ischemic event such as death, recurrent infarction, angina or repeat revascularization. The pathophysiological processes particular to the CABG procedure that may affect graft endothelial function are most active in the early phase after surgery. Angiotensin-converting enzyme (ACE) inhibition has been shown to be effective in reducing or preventing ischemic events in patients with and without left ventricular dysfunction, and in those at high risk for CAD. Nonetheless, no large clinical trail has investigated this role of ACE inhibition in preventing ischemic events early after CABG. OBJECTIVE: The Ischemia Management with Accupril post bypass Graft via Inhibition of angiotensin coNverting Enzyme (IMAGINE) study addressed whether ACE inhibition initiated early after CABG improves short and long term outcomes in patients after CABG. PATIENTS AND METHODS: This multicentre, multinational trial recruited 2204 patients with an uncomplicated course early after CABG from 55 to 65 medical care facilities in Canada, The Netherlands, Belgium and France. Eligible patients with normal left ventricular function were randomly assigned to placebo or quinapril (titrated up to 40 mg daily where possible) within seven to 10 days after CABG. All patients were followed up closely for a minimum of 12 months after random placement. The median treatment period is expected to be approximately 27 months.
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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 | 0.017 | 0.011 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".