Improving Surgical Methods for Studying Vascular Grafts in Animal Models
Bibliographic record
Abstract
While clinical vascular grafting uses an end-to-side surgical method, researchers primarily use end-to-end implant techniques in preclinical models. This may be due in part to the limitations of using small animal models in research. The work presented here provides support and evidence for the improvement of vascular graft implant techniques by demonstrating the successful implantation of experimental grafts into both large and small animal models. Specifically, models of aortoiliac baboon (Papio anubis) bypass and common carotid rabbit (Oryctolagus cuniculus) bypass were used to test vascular grafts for thrombosis and vascular healing after 1 month using an end-to-side anastomosis grafting procedure. Patency was evaluated with ultrasound or histological techniques, and neointimal growth was quantified with histology. In the development of this procedure for small animals, both an end-to-end/end-to-side and an end-to-side/end-to-side configuration were tested in rabbits. One hundred percent of rabbit implants (2/2) with an end-to-end/end-to-side configuration were patent at explant. However, with the end-to-side/end-to-side configuration, 66% (6/9) of rabbit implants and 93% (13/14) of baboon implants remained patent at 1 month, suggesting the importance of replicating the end-to-side method for testing vascular grafts for clinical use. This study describes feasible preclinical surgical procedures, which simulate clinical vascular bypass grafts even in small animals. Widespread implementation of these end-to-side surgical techniques in these or other animals should improve the quality of experimental, preclinical testing and ultimately increase the likelihood of translating new vascular graft technologies into clinical applications.
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 | 0.011 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".