Midterm clinical outcomes with everolimus-eluting bioresorbable scaffolds versus everolimus-eluting metallic stents for percutaneous coronary interventions: a meta-analysis of randomised trials
Bibliographic record
Abstract
bioresorbable vascular scaffold (BVS) versus an everolimus-eluting metallic stent (EES) for percutaneous coronary interventions. METHODS AND RESULTS: We performed a meta-analysis of aggregate data by searching Medline, EMBASE, Cochrane databases and proceedings of international meetings for randomised trials reporting the clinical outcomes beyond one year of patients treated with BVS versus EES. The primary efficacy and safety outcomes were target lesion failure (TLF) and definite/probable stent (scaffold) thrombosis (ST), respectively. Secondary outcomes were the individual components of the primary efficacy outcome (cardiac death, target vessel myocardial infarction [MI], and ischaemia-driven target lesion revascularisation [ID-TLR]). A total of 5,583 patients randomly received BVS (n=3,261) or EES (n=2,322) in seven trials. Weighted median follow-up was 26.6 months. Patients treated with BVS versus EES showed a higher risk of TLF (odds ratio [OR] 1.35, 95% confidence interval [CI]: 1.11-1.65; p=0.0028) due to a higher risk of target vessel MI (OR 1.68, 95% CI: 1.21-2.33; p=0.008) and ID-TLR (OR 1.42, 95% CI: 1.10-1.84; p=0.007) though the risk for cardiac death was not statistically different (OR 0.89, 95% CI: 0.55-1.43; p=0.56). Patients treated with BVS versus EES showed a higher risk of definite/probable ST (OR 3.24, 95% CI: 1.92-5.49; p<0.0001), particularly in the period beyond one year after implantation (OR 4.03, 95% CI: 1.49-10.87; p=0.006). CONCLUSIONS: At midterm follow-up, patients treated with BVS as compared to those treated with EES display a higher risk of target lesion failure and scaffold thrombosis.
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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 | 0.015 | 0.020 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.048 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".