Impact of Patient and Target-Vessel Characteristics on Arterial and Venous Bypass Graft Patency
Bibliographic record
Abstract
BACKGROUND: The purpose of this investigation was to determine optimal patient and target-vessel characteristics to maximize arterial and venous graft patency on the basis of data from a large clinical trial. METHODS AND RESULTS: Angiographic data on 440 radial artery grafts and 440 saphenous vein grafts were analyzed with methodology to account for within-patient clustering. Multivariable models that incorporated patient demographic, operative, anatomic, and postdischarge medical management were constructed to determine predictors of graft occlusion. Radial artery use was strongly protective against graft occlusion at 1 year after adjustment for all covariates, with a larger protective effect seen in women (P=0.05 for a subgroup-by-treatment interaction). Among all grafts, diabetes and small target-vessel diameter were associated with an increased risk of graft occlusion, and grafting to a target vessel with more severe proximal stenosis was associated with a decreased risk of graft occlusion. With regard to gender, radial artery graft occlusion at 1 year occurred in similar proportions of men (8.6%) and women (5.3%, P=0.6), whereas, for saphenous vein grafts the comparable occlusion rates were 12.0% and 23.3% respectively (P=0.02). A history of peripheral vascular disease was associated with an elevated risk of radial artery occlusion but was not associated with early vein graft occlusion (P=0.02 for a subgroup-by-treatment interaction). CONCLUSIONS: Patients benefit from radial artery-coronary artery bypass conduits as opposed to saphenous vein conduits, and this effect is especially strong in women. Small target-vessel size adversely affected graft patency, and grafting to a target vessel with more severe proximal stenosis improved graft 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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".