Experimental validation of more realistic computer models for stent‐graft repair of abdominal aortic aneurysms, including pre‐load assessment
Bibliographic record
Abstract
Although the endovascular repair of abdominal aortic aneurysms is a less invasive alternative than classic open surgery, complications such as endoleak and kinking still need to be addressed. Numerical simulation of endovascular repair is becoming a valuable tool in stent-graft (SG) optimization, patient selection and surgical planning. The experimental and numerical forces required to produce SG deformations were compared in a range of in vivo conditions in the present study. The deformation modes investigated were: bending as well as axial, transversal and radial compressions. In particular, an original method was developed to efficiently account for radial pre-load because of the pre-compression of stents to match the graft dimensions during manufacturing. This is important in order to compute the radial force exerted on the vessel after deployment more accurately. Variations of displacement between the experimental and numerical results ranged from 1.39% for simple leg bending to 5.93% for three-point body bending. Finally, radial pre-load was modeled by increasing Young's modulus of each stent. On average, it was found that Young's modulus had to be augmented by a factor of 2. Copyright © 2016 John Wiley & Sons, Ltd.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".