Numerical simulations of stent-based local drug delivery : 2D geometric investigations and the evaluation of 3D designs on the basis of local delivery effectiveness
Bibliographic record
Abstract
Drug-eluting coronary stents have been identified as a promising means of treating in-stent restenosis. Animal models used to investigate these devices can deliver results for restenosis rates, but cannot provide accurate dose delivery information which would be very useful for refining stent geometries and apposition techniques to optimize dose delivery. In this study, a two-dimensional numerical model is constructed to explore geometrical situations of interest following stent implantation. Metallic stents with a polymer coating and biodegradable solid polymeric stents represent two different vehicles for local delivery. A comparison between these stent types is carried out through the variation of geometric parameters of interest. An investigation of solid polymeric stent struts in a curved vessel is then done through a comparison of dose delivery characteristics on inner and outer walls. Dose delivery success is measured using three quantities: the dose homogeneity in a defined therapeutic region, the percentage of mass remaining in that therapeutic region after a defined therapeutic duration, and the amount of contact between the stent and the vascular wall. The appropriateness of a quasi-stationary hypothesis is then justified in two dimensions through analysis of flow and diffusion parameters. This simplification is applied to stent geometries in three dimensions and a single local delivery effectiveness score based on the three dose delivery parameters is calculated. This tool for evaluating stent designs on the basis of local delivery effectiveness provides a starting point for similar, more sophisticated methods that could eventually be applied to a larger sample of existing stent geometries. Ultimately, the output of such a tool could be used to optimize drug-eluting stent designs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".