Development of a high fidelity pressurized porcine beating heart simulator for cardiac surgery training
Bibliographic record
Abstract
BACKGROUND: Development of a high-fidelity cardiac surgery simulator (CSS) requires integration of a heart model with a mock cardiopulmonary bypass (CPB) circuit that can provide feedback to mimic the pathophysiology of cardiac surgery. However, the cost of commercially available simulators precludes regular use. We describe steps in the construction of a high-fidelity CSS that integrates a pulsatile paracorporeal ventricular-assist device (Pulse-VAD) and a commercially available CPB simulator. METHODS/RESULTS: simulator, as well as Pulse-VAD cannulation (fitted with inflatable balloons) of both ventricles. After each simulation run, the team addressed key issues to derive successive model changes through consensus. Key modifications included: a) pressure maintenance of the cardiac chambers (removal of lungs, Pulse-VAD cannulation sites at the left pulmonary artery and vein, double ligation of arch vessels); b) high-fidelity beating of both ventricles (full Pulse-VAD bladder filling and ensuring balloon neck placement at the valvular plane) and c) reproducible management of porcine anatomy (management of porcine aorta, ligation of left azygous vein and shortened ascending thoracic aortic segment). CONCLUSION: A CSS can be prepared at low cost, with integration into a high-fidelity CPB simulator with a novel beating heart component. This setup can be used in teaching the basics of CPB techniques and complex surgical procedures. Future work is needed to validate this model as a simulation instrument.
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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 | 0.002 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".