Lipid-lowering therapy and coronary artery bypass graft surgery
Bibliographic record
Abstract
PURPOSE OF REVIEW: Despite their apparent benefits, statins remain underutilized after coronary artery bypass graft (CABG) surgery. To summarize the literature regarding statin therapy and CABG, we performed a systematic review of the Medline database from 1987-2011 to assess the benefits of statins in CABG patients, including the role of high-dose therapy, and highlight areas for future study. RECENT FINDINGS: When administered prior to CABG, statins reduce the risk of perioperative mortality, stroke, and atrial fibrillation. After CABG, statins limit the progression of atherosclerosis in native coronary arteries, inhibit the process of saphenous vein graft disease, and improve vein graft patency. Furthermore, postoperative statins reduce the recurrence of cardiovascular events and improve all-cause mortality. High-intensity statin therapy early after surgery may benefit CABG patients, but this is yet to be evaluated prospectively. SUMMARY: Statins clearly improve the outcomes of CABG patients. In the absence of contraindications, all patients undergoing CABG are candidates for life-long statin therapy, with initiation recommended as soon as coronary disease is documented. Statins should be restarted early after surgery. However, the optimal postoperative lipid-lowering regimen remains unknown and should be the subject of upcoming trials. Strategies directed toward improving statin prescription rates and patient adherence should also be priorities for future research.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| 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) | 0.005 | 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".