Off pump coronary artery bypass graft surgical simulation development
Bibliographic record
Abstract
Coronary artery bypass grafting (CABG) is one of the most commonly performed major cardiac surgeries in North America.1 The standard CABG surgical approach is performed on pump; however, advances in medical mechatronics and surgical techniques have made the option of performing CABG without putting the patient on bypass and stopping the heart to be a viable strategy. The off-pump method (OP-CABG) provides patients with superior long-term results and is now being preferred.2 Due to the minimally-invasive and dynamic nature of the off-pump method, appropriate surgical training for OP-CABG is becoming increasingly important. However, current training apparatuses used by the medical industry and academia, along with various ethical and sourcing concerns, are lacking in the ability to replicate the motion of a beating heart, a clear requirement for an effective OP-CABG training environment. In this work, we propose the development of a mechanical OP-CABG surgical training device with disposable (i.e. replaceable) synthetic coronary arteries resting on a silicone heart surface. Cardiac movement is simulated via targeted and timed tensioning and releasing of multiple pull cords operated by a servo-motor-driven crankshaft. Using gated 4D CT, we demonstrate that our beating heart model mimics 3D heart motion. To our knowledge, this is the first mechanical OP-CABG simulator that replicates cardiac motion, providing a novel and effective tool for enhancing surgical training.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".