Finite element methods to analyze helical stent expansion
Bibliographic record
Abstract
Helical polymeric stents have been proposed as a suitable geometry for biodegradable drug-eluting polymer-based stents. However, helical stents often experience nonuniform local expansion (dog boning), which can prohibit full stent expansion using conventional methods. The development of stents and deployment methods is challenging and can be supported by numerical analysis; however, this complex problem is often approached with simplified boundary conditions that may not be appropriate for helical stents. The finite element method (explicit and implicit) was used to investigate three common stent expansion approaches with a focus on helical stent geometry, which differs from traditional wire mesh stent expansion. Although each of the three methods considered provided some insight into the expansion characteristics, common displacement controlled, and uniform expansion methods were not able to demonstrate the characteristic local deformations observed in expansion. A coupled stent-balloon model, although computationally expensive, was able to demonstrate the expected nonuniform deformation. To address nonuniform expansion, a progressive expansion approach has been investigated and verified numerically. This method may also provide a suitable solution for nonuniform expansion in other stent designs by minimizing loading and potential damage to the artery that can occur during stent deployment.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".