Measurement of Frictional Properties of Aortic Stent Grafts and Their Delivery Systems
Bibliographic record
Abstract
Stent grafts are medical devices used to treat abdominal aortic aneurysms (AAAs) in endovascular aneurysm repair (EVAR). Computational and experimental models have been developed to study stent graft delivery and deployment during EVAR; however, frictional properties have not been taken into account in most previous studies. The objective of this study was to determine the coefficients of friction of three commercially available stent grafts (Cook Zenith, Medtronic Endurant, and Vascutek Anaconda), their delivery sheaths, a porcine aorta, and two mock arterial materials. Stent grafts were obtained and separated into stents, graft fabric, and sheaths. Using a custom-made friction measurement apparatus, the coefficients of friction were measured between five material pairs: (i) the stents and inner surface of the sheath, (ii) the graft fabric and inner surface of the sheath, (iii) the outer surface of the sheath and a porcine aorta, (iv) the outer surface of the sheath and three different polyvinyl alcohol (PVA) cryogels, and (v) the outer surface of the sheath and a polydimethylsiloxane (PDMS) sheet. The results show that the coefficients of friction between the graft fabric and the sheath were higher than those between the stents and the sheath. The PVA cryogels showed more comparable frictional properties to the porcine aorta than did the PDMS sheet, suggesting that PVA cryogels provide a more accurate approximation for the in vivo frictional properties. These results can be used to improve the accuracy of computational models for stent graft delivery and deployment and to select appropriate materials for vascular phantoms.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".