Everolimus-Eluting versus Paclitaxel-Eluting Stents in Percutaneous Coronary Intervention: Meta-Analysis of Randomized Trials
Bibliographic record
Abstract
Background. Individual randomized trials have suggested that everolimus-eluting stents may have improved clinical outcomes compared to paclitaxel-eluting stents, but individual trials are underpowered to examine outcomes such as mortality and very late stent thrombosis. Methods. Medline, Cochrane, and conference proceedings were searched for randomized trials comparing everolimus versus paclitaxel-eluting stents for percutaneous coronary intervention. Results. 6792 patients were included from 4 randomized controlled trials. Stent thrombosis was reduced with everolimus stents versus paclitaxel stents (0.7% versus 2.3%; OR: 0.32; CI: 0.20-0.51; P < 0.00001). The reductions in stent thrombosis were observed in (i) early stent thrombosis (within 30 days) (0.2% versus 0.9%; OR: 0.24; P = 0.0005), (ii) late (day 31-365) (0.2% versus 0.6%; OR: 0.32; P = 0.01), and (iii) very late stent thrombosis (>365 days) (0.2% versus 0.8%; OR: 0.34; P = 0.009). The rates of cardiovascular mortality were 1.2% in everolimus group and 1.6% in paclitaxel group (OR: 0.85; P = 0.43). Patients receiving everolimus-eluting stents had significantly lower myocardial infarction events and target vessel revascularization as compared to paclitaxel-eluting stents. Interpretation. Everolimus compared to paclitaxel-eluting stents reduced the incidence of early, late, and very late stent thrombosis as well as target vessel revascularization.
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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 | 0.026 | 0.052 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.047 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".