A Novel Silicone Simulation Model for Microvascular Anastomosis
Bibliographic record
Abstract
Purpose: Surgical simulation of microvascular anastomosis has become increasingly popular. There are several living and silicone models available. Current silicone models fail to accurately reproduce a vessel's loose adventitial layer, which may lead to the development of improper microsurgical technique. Our purpose is to create a realistic 3-dimensional microsurgical simulator that incorporates an adventitial vessel layer for higher fidelity manipulation of vessels. Methods: A microvascular anastomosis simulator was manufactured using metal moulds and inorganic materials. Synthetic tubing was created with a metal cylinder, 1.65 mm in diameter, painted with 2 sequential layers of silicon with a shore hardness of 2A. Silicone was allowed to fully cure in-between layers. Vessel adventitia was created with a 100-micron polyester mesh adhered to the silicone vessel exterior. Once dry, the synthetic tube is removed from the metal cylinder is then clipped to reveal the inner lumen. Both Resident and attending physicians evaluated the model with and without the adventitial layer and completed a questionnaire. Results: = .0232). The analysis included: simulation realism, educational utility, and overall satisfaction. Responses in all domains were favourable, suggesting the utility of this model. Conclusion: We created a realistic, high fidelity microvascular anastomosis simulator that is low cost and easily reproducible. Initial feedback is encouraging regarding realism, educational utility, and overall usefulness. Further validation is required to assess its effectiveness in resident education and skill transfer to the operating room.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 teacher head, 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".