Anthropomorphic and biomechanical mockup for abdominal aortic aneurysm
Bibliographic record
Abstract
Abdominal aortic aneurysm (AAA) is an asymptomatic condition due to the dilation of abdominal aorta along with progressive wall degeneration, where rupture of AAA is life-threatening. Failures of AAA endovascular repair (EVAR) reflect our inadequate knowledge about the complex interaction between the aortic wall and medical devices. In this regard, we are presenting a hydrogel-based anthropomorphic mockup (AMM) to better understand the biomechanical constraints during EVAR. By adjusting the cryogenic treatments, we tailored the hydrogel to mimic the mechanical behavior of human AAA wall, thrombus and abdominal fat. A specific molding sequence and a pressurizing system were designed to reproduce the geometrical and diseased characteristics of AAA. A mechanically, anatomically and pathologically realistic AMM for AAA was developed for the first time, EVAR experiments were then performed with and without the surrounding fat. Substantial displacements of the aortic centerlines and vessel expansion were observed in the case without surrounding fat, revealing an essential framework created by the surrounding fat to account for the interactions with medical devices. In conclusion, the importance to consider surrounding tissue for the global deformation of AAA during EVAR was highlighted. Furthermore, potential use of this AMM for medical training was also suggested.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".