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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".