Assessment of Large Animal Vascular Dimensions for Intra-Aortic Device Research and Development: A Systematic Review
Bibliographic record
Abstract
Animal studies are often required to evaluate new cardiovascular medical devices before they reach the market. Moreover, first-generation novel devices including aortic endovascular prostheses and circulatory support devices are often larger than later iterations or tested in a limited range of sizes. One of the challenges in evaluating these devices is finding a model that is both accessible and anatomically similar to humans, as there is a paucity of data on vascular dimensions in large animals. We set out to complete a comprehensive review of available reports on vascular dimensions in swine, ovine, and bovine models, with a particular focus on the descending aorta and ilio-femoral arteries. We searched Embase and MEDLINE databases for reports of descending aorta and peripheral vascular dimension in large animal models. Data from swine, ovine, and bovine models were separated by weight into 3 categories: 40 to 60 kg, 61 to 80 kg, and >80 kg. We also incorporate our computed tomography angiography data from 4 large sheep and 9 calves into this review. Swine, sheep, and calf >80 kg may serve as the best models to maximize aortic diameter resemblance to humans. If device implantation can be achieved in aortas of smaller dimensions, care should be taken to ensure access site suitability such as the common femoral artery in these smaller animals.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".