Graft patency of no-touch versus conventionally harvested saphenous vein conduits in coronary artery bypass grafting: A frequentist and Bayesian meta-analysis of randomized trials
Bibliographic record
Abstract
Background No-touch (NT) saphenous vein harvest is a technique that minimizes intimal injury and has been shown to improve patency. This study aimed to directly compare NT saphenous vein grafts (SVGs) to conventional skeletonized (CON) SVGs through a meta-analysis. Methods A systematic literature search was conducted for randomized controlled trials comparing the angiographic patency of NT-SVG and CON-SVG. The primary outcome was graft occlusion as a proportion of the total grafts assessed. Secondary outcomes were graft occlusion per patient, all-cause mortality, and leg wound complications. A random-effects model using a frequentist approach and Bayesian analysis were performed. Results A total of 235 studies were retrieved, of which 7 ultimately were chosen for analysis, with a total of 3334 randomized patients and 5798 SVGs. The pooled estimated age was 63.5 and 62.8 years for NT and CON, respectively, with approximately 14% of patients being women. The weighted mean angiographic follow-up was 11.6 months. Relative to CON-SVG, NT-SVG was associated with lower rates of graft occlusion per graft (relative risk [RR], 0.57; 95% confidence interval [CI], 0.46-0.72; P < .001) and per patient (RR, 0.61; 95% CI, 0.46–0.79; P < .001), comparable all-cause mortality (RR, 1.12; 95% CI, 0.56-2.25; P = .75), and a higher rate of leg wound complications (RR, 2.32; 95% CI, 1.78-3.02; P < .001). Findings for occlusion per graft were consistent with Bayesian analysis (RR, 0.57; 95% credible interval, 0.41-0.79). Conclusions Compared to CON, NT confers significantly better patency and equivalent survival but poorer harvest site healing. The clinical benefit of NT remains uncertain, and further evidence is needed.
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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 | 0.046 | 0.080 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.057 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".