Review of stent coating strategies: Clinical insights
Bibliographic record
Abstract
Despite advances in stent design, expansion techniques and anti-thrombotic agents to improve pharmacological control of subacute thrombosis (SAT) and to reduce to 2 the occlusive thrombosis rates, a significant risk of mortality associated with thrombotic vascular occlusion due to the adhesion of blood constituents remains a problem for patients with more complex lesions. The adhesion process is greatly governed by the surface characteristics, mainly the surface chemical composition, surface morphology, presence of charge, surface wettability and surface roughness. Surface chemical inertness (reduced interaction with chemicals and biological components) subsequently became the primary criteria which guided the development of non thrombotic stents as well as other blood-contacting materials. A number of strategies have been adopted in an effort to coat the stent with or without the use of a drug delivery system, to overcome the thrombus formation, to minimize the stent occlusion and to improve the overall hemocompatibility of the device. This paper aims at reviewing the clinical outcomes of main non-pharmaceutical stent coating procedures and their clinical outcomes. New stents which combine the anti-thrombotic coating with the drug delivery ability, such as radioactive stents, degradable stents and some new challenging trends which are mostly at research and development stage for stent surface coatings are also introduced.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".