Drug-Eluting or Bare Metal Stents for the Treatment of Saphenous Vein Graft Disease
Bibliographic record
Abstract
BACKGROUND: Observational studies and randomized, controlled trials have yielded uncertain results regarding the benefits of drug-eluting stents (DES) for the treatment of saphenous vein graft (SVG) disease. The objective of this meta-analysis was to assess the cumulative evidence regarding the efficacy and effectiveness of DES to treat SVG compared with bare metal stent (BMS). METHODS AND RESULTS: We conducted a bayesian hierarchical meta-analysis of all randomized, controlled trials and observational studies that compared clinical outcomes after DES or BMS placement in SVG disease. Our search resulted in 25 studies, cumulating 5755 patients. DES implantation was not associated with an increased risk of death (odds ratio [OR], 0.85; 95% credible intervals (CrI) [CrI], 0.62 to 1.21) or myocardial infarction (OR, 0.83; 95% CrI, 0.56 to 1.32), but wide CrIs preclude definitive conclusions. Target vessel revascularization (OR, 0.55; 95% CrI, 0.39 to 0.76) and target lesion revascularization (OR, 0.58; 95% CrI, 0.37 to 0.87) were both reduced by approximately 45% with DES. When combining these outcomes, the OR for major adverse cardiac events was reduced in patients treated with DES (OR, 0.62; 95% CrI, 0.46 to 0.81). Finally, the relative risk of stent thrombosis appeared lower with DES, although again the CrIs were very wide (OR, 0.54; 95% CrI, 0.13 to 1.39). CONCLUSIONS: In this study-level meta-analysis, the largest ever reported and the first using bayesian methods, the use of DES for the treatment of SVG disease reduces target vessel revascularization and target lesion revascularization procedures compared with BMS. Although there is no evidence to date to suggest increased rates of mortality, myocardial infarction, or stent thrombosis, further data are needed to address this safety issue.
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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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.011 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".