Revascularization in left ventricular dysfunction
Bibliographic record
Abstract
PURPOSE OF REVIEW: The purpose of this article is to provide an overview of revascularization in patients with coronary artery disease (CAD) and left ventricular dysfunction (LVD). RECENT FINDINGS: Patients with significant CAD and LVD are a high-risk patient population. They make up a minority of the cases from the largest, prospective coronary revascularization trials. The Surgical Treatment for Ischemic Heart Failure (STICH) Trial and its substudies are the most important and well cited in this field. The 10-year data from STICH showed that surgical revascularization was associated with lower all-cause mortality compared with medical therapy. Several smaller studies have confirmed that surgical revascularization carries a significant risk of short-term mortality but overall improved long-term outcomes in patients with LVD. Data from multiple observational studies further confirm that coronary artery bypass graft (CABG) is superior to percutaneous coronary revascularization for long-term survival and freedom from repeat revascularization in patients with LVD. We suggest that patients with LVD undergoing CABG should be considered for multiarterial grafting and that some patients may benefit from an off-pump procedure. SUMMARY: Surgical revascularization confers a long-term survival benefit in patients with significant CAD and LVD. Further studies will be needed to precisely determine the ideal candidate for surgical versus percutaneous revascularization.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".