New synthetic mitral valve model for human prolapsed mitral valve reconstructive surgery for training
Bibliographic record
Abstract
The training process of young surgeons is highly desirable in order for them to gain an understanding of the quality of care and patient safety required during cardiac surgeries, however, it may take a few years of practice in order for them to properly develop these skills. Artificial life-like platforms and models are extremely recommended for teaching hands-on and real-world practice in both junior and even experienced medical professionals and surgeons. Suitable and accessible training tools are of significant importance for simulating a particular surgery in order to provide practice opportunities for a specific surgical procedure. In this study, we focussed on the simulation of the human mitral valve prolapse reconstructive surgery. An innovative, artificial, biomimetic model was designed and fabricated made of Cryogel biomaterial developed in our lab that is suitable for the precise practice on the mitral valve prolapse model. The proposed model is mainly made up of polyvinyl alcohol (PVA) cryogel, which is designed to resemble the geometric and mechanical properties of a diseased (prolapse) mitral valve. We simulated the constructive prolapsed mitral valve surgery entirely on a synthetic platform. The platform was made available to four certified cardiac surgeon and there were unanimously very positive with no considerable differences in the procedural assessments between them. The proposed model has a similar appearance and texture to that of a diseased mitral valve and holds consistent mechanical properties to those of the real tissue. The offered technology may be used for other cardiothoracic reconstructive surgeries with high precision and consistency.
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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 | 0.000 | 0.000 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".