Development of a dynamic Chest Wall and operating table simulator to enhance congenital heart surgery simulation
Bibliographic record
Abstract
BACKGROUND: The Hands-On Surgical Training in Congenital Heart Surgery (HOST-CHS) program using 3D printed heart models has received positive feedback from attendees. However, improvements were necessary in the simulator set up to replicate the ergonomics experienced in the operating room. This paper illustrates the development of a dynamic chest wall and operating table simulator to enhance the simulation experience. METHODS: The simulator was designed to address the limitations with the existing set up. This included a suboptimal operating position, unrealistic surgical exposure and limitations in illuminating the operative field and recording procedures. A combination of computer-aided design and various 3D-printing techniques were used to build the components. The simulator's usefulness was evaluated by surgeons who attended the 5th annual HOST course via a questionnaire. RESULTS: The simulator consists of three components; an operating table simulator which allows height adjustment and pitch-and-roll motion; a suture retraction disc, which holds sutures under tension to improve exposure; and a pediatric chest wall cavity to replicate a surgeon's access experience during surgery. Nineteen surgeons completed the questionnaire. All surgeons agreed that the addition of the simulator was acceptable for surgical simulation and that it helped replicate the ergonomics experienced in the operating room. CONCLUSIONS: The inclusion of the HOST-CHS simulator adds value to simulation in congenital heart surgery (CHS) as it replicates the view and exposure a surgeon experiences. Improvements like these will help develop high-fidelity simulation programs in CHS, which could be utilized to train surgeons globally.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".