Increased patency with comparable mortality and revascularization risk: Is the case for no-touch vein harvesting open and shut?
Bibliographic record
Abstract
Since the introduction of the saphenous vein graft (SVG) for coronary artery bypass grafting (CABG) in 19621, the SVG has remained the most commonly used conduit to the non-LAD territories for more than half a century. However, several issues surrounding the use of SVGs, including higher graft occlusion rates and wound complications from the harvesting process, have been identified in clinical practice. As such, significant interest has been dedicated towards developing harvesting techniques that minimize the risk of these acute and late complications. In this issue of the Journal of Cardiac Surgery, Yokoyama and colleagues compared the impact of open vein harvesting (OVH), endoscopic vein harvesting (EVH) and no-touch vein harvesting (NT) on all-cause mortality, revascularization and graft failure, using a network meta-analysis based on randomized controlled trials and propensity-score matched studies. The results showed that the risk of graft failure was approximately halved amongst patients receiving NT compared with EVH and OVH; importantly, though, NT was not associated with lower all-cause mortality or revascularization risk. To further examine whether the use of NT grafts endow patients with better long-term clinical outcomes, such as mortality, myocardial infarction, and revascularization rates, a large-scaled randomized controlled trial or a patient-level combined meta-analysis is required.
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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 | 0.026 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 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".