Overlapping paclitaxel-eluting stents: Long-term effects in a porcine coronary artery model
Bibliographic record
Abstract
OBJECTIVE: At 4-year follow-up, paclitaxel-eluting stents (PES, TAXUS) have demonstrated clinical effectiveness in reducing restenosis without increasing death or myocardial infarction. Concerns remain with all drug-eluting stents, however, regarding potential interference with long-term healing, particularly in zones with adjacent stent overlap due to theoretical doubling in both drug release and tissue contact with coating polymer. Therefore, we evaluated long-term healing of overlapped TAXUS stents in an accepted animal model. METHODS: Seventy-one non-injured swine underwent coronary artery placement of 138 overlapping stent-pairs (91 PES TAXUS Liberté 1 microg/mm(2) slow release formulation, 3.0 or 3.5 mm diameter pairs and 47 control bare metal Liberté pairs) deployed at a 1.1:1 to 1.2:1 target stent-to-artery diameter ratio. Pathological analysis was performed at 30 (9 bare, 10 paclitaxel), 90 (9 bare, 10 paclitaxel), 180 (10 bare, 16 paclitaxel), 360 (10 bare, 21 paclitaxel), and 580 (9 bare, 22 paclitaxel) days. RESULTS: At all time intervals overlapped TAXUS stents were consistently endothelialized and free of luminal thrombus or vascular dilatation. Full healing, however, was delayed compared to control, with macrophage processed para-strut fibrin and cellular debris still present, but reduced and sequestered from blood flow by an endothelialized neointima at 360 and 580 days. While neointimal thickness in TAXUS overlap zones was significantly less than control at 30 days, greater neointima formation was observed with TAXUS at > or =90 days, but was stable and did not progress further from 90 to 580 days. CONCLUSION: In this porcine model TAXUS stents demonstrated safety and acceptable healing with prolonged time to resolution of para-strut deposits, and did not produce the sustained neointimal suppression seen clinically.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".