Angiographic Patency of Coronary Artery Bypass Conduits: An Updated Network Meta-Analysis of Randomized Trials
Bibliographic record
Abstract
INTRODUCTION: The second best conduit for coronary artery bypass grafting is uncertain. The objective of this study is to determine the second best conduit according to graft patency results from randomized controlled trials using a network meta-analysis. METHODS: A systematic literature search was conducted for randomized controlled trials comparing the angiographic patency rate of the no-touch saphenous vein (NT-SV), the radial artery (RA), the right internal thoracic artery (RITA), and the gastroepiploic artery (GEA) in reference to the conventionally harvested saphenous vein (CON-SV). The primary outcome was graft occlusion, and the secondary outcome was all-cause mortality. RESULTS: A total of 859 studies were retrieved, of which 18 were included. A total of 6,543 patients and 8,272 grafts were analyzed. The weighted mean angiographic follow-up time was 3.5 years. Compared with CON-SV, RA (incidence rate ratio [IRR] 0.56; 95% confidence interval [CI], 0.43-0.74) and NT-SV (IRR 0.56; 95% CI, 0.44-0.70) demonstrated lower graft occlusion. NT-SV and RA were ranked as the best conduits (rank score for NT-SV 0.88 vs. 0.87 for RA, 0.29 for GEA, 0.27 for CON-SV, and 0.20 for RITA). There was no significant difference in late mortality between different conduit types. CONCLUSION: RA and NT-SV are associated with significantly lower graft occlusion rates and are comparably ranked as the best conduit for patency.
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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 | 0.028 | 0.065 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.033 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".