Preclinical Models of Restenosis and Their Application in the Evaluation of Drug-Eluting Stent Systems
Bibliographic record
Abstract
Coronary arterial disease (CAD) is the leading cause of death in the United States, the European Union, and Canada. Percutaneous coronary intervention (PCI) has revolutionized the treatment of CAD, and it is the advent of drug-eluting stent (DES) systems that has effectively allayed much of the challenge of restenosis that has plagued the success of PCI through its 30-year history. However, DES systems have not been a panacea: There yet remain the challenges associated with interventions involving bare metallic stents as well as newly arisen concerns related to the application of DES systems. To effectively address these novel and ongoing issues, animal models are relied on both to project the safety and efficacy of endovascular devices and to provide insight into the pathophysiology underlying the vascular response to injury and mechanisms of restenosis. In this review, preclinical models of restenosis are presented, and their application and limitation in the evaluation of device-based interventional technologies for the treatment of CAD are discussed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".