Development of a high fidelity pressurized porcine beating heart simulator for cardiac surgery training
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 it