Storage solutions to improve grafts preservation and longevity in coronary artery bypass grafting surgery: hype or hope?
Bibliographic record
Abstract
PURPOSE OF REVIEW: Saphenous vein grafts (SVGs) remain the most-commonly used conduits for coronary artery bypass grafting (CABG).Significant rates of vein graft failure (VGF) remain a limitation of their use as this diminishes the long-term benefits of CABG. The choice of intraoperative SVGs preservation solution is believed to have an impact on graft patency; however, the superiority of one solution over the others remains in question. RECENT FINDINGS: In the present review, we describe the pathophysiological mechanisms underlying the different phases of VGF. We also reviewed the most recent literature comparing and evaluating the efficacy of various storage solutions. These include heparinized saline, autologous heparinized blood, buffered solutions, and crystalloid cardioplegia. SUMMARY: It is clear that the composition of the SVGs storage solution has an impact on vessel wall structure and function. There is a lack of translational and clinical research on the topic; thus, conclusions cannot be drawn regarding the superiority of one solution over the others in terms of VGF. Future research needs to be conducted to address this gap in the literature in order to make meaningful evidence-based recommendations on intraoperative graft storage.
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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 | 0.003 |
| 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".