Plaque sealing with drug-eluting stents versus medical therapy for treating intermediate non-obstructive saphenous vein graft lesions: A pooled analysis of the VELETI and VELETI II trials
Bibliographic record
Abstract
BACKGROUND: The presence of intermediate "non-obstructive" saphenous vein graft (SVG) lesions is a strong predictor of cardiac events. We wanted to assess the efficacy of sealing these SVG lesions with drug-eluting stent (DES) implantation for reducing major adverse cardiac event (MACE) rate. METHODS: The present analysis is based on the pooled data from the VELETI and VELETI II randomized trials. Patients with at least 1 intermediate SVG lesion (30%-60% diameter stenosis) were randomized to DES implantation (SVG-DES) or medical treatment (SVG-MT). The primary outcome was the first occurrence of MACE, defined as the composite of cardiac death, myocardial infarction, or coronary revascularization related to the target SVG. RESULTS: A total of 182 patients were included (mean age, 70 ± 9 years), with 90 and 92 patients allocated to the SVG-DES and SVG-MT groups, respectively. After a mean follow-up of 4 ± 1 years, patients in the SVG-MT group exhibited a higher rate of MACE related to the target SVG (23.9% vs 17.8% in the SVG-DES group; P=.04) and MACE related to the target SVG lesion (21.7% vs 12.2% in the SVG-DES group; P<.01). In the multivariable analysis, a higher total cholesterol value at baseline (P=.04) was the only independent predictor of SVG disease progression leading to clinical events. CONCLUSIONS: In patients with prior coronary artery bypass grafting and intermediate non-obstructive SVG lesions, plaque sealing with DES reduced the incidence of MACE related to SVG disease progression. A higher cholesterol level was the main predictor of SVG disease progression leading to clinical events in these patients.
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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 | 0.014 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.019 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".