State-of-the-art silicone molded models for simulation of arterial switch operation: Innovation with parting-and-assembly strategy
Bibliographic record
Abstract
Background: Three-dimensional (3D) printed models are widely accepted for use in training of various surgical procedures for congenital heart disease; however, their physical properties have been considered suboptimum for procedures. We created silicone molded models produced using a novel "parting and assembly" strategy and compared their suitability for hands-on training with that of conventional 3D printed models. Methods: Computed tomography imaging data from 2 patients with transposition of the great arteries were used. The heart was divided into multiple parts (atria, ventricles, great arteries, coronary arteries, and valves), and molds of each part were created. The parts reproduced by silicone molding were assembled using an adhesive agent. In an online course, 2 silicone molded models and 1 3D printed model were used for training of 34 surgeons. A questionnaire was distributed to these surgeons aimed at assessing the suitability of the models for the arterial switch operation (ASO). Results: The silicone molded models showed excellent anatomic detail, high elasticity, and high resistance to tearing. The cost per model, based on the production of 50 models, was slightly higher for the silicone molded models compared with the 3D printed models. All 26 surgeons who completed the questionnaire reported that the silicone molded models provided sufficient anatomic information, but only 19% said the same for the 3D printed models. All surgeons also considered the silicone models to be realistic when passing a needle, cutting vessels, suturing, and excision of the coronary buttons, as opposed to <46% for the 3D printed models. Conclusions: Silicone molding of models for the ASO is feasible by applying a "parting and assembly" strategy. Silicone molded models provide excellent physical properties that are far superior to those of 3D printed models for surgical simulation.
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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 | 0.001 | 0.001 |
| 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) | 0.002 | 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".