Design of an electromechanical pump system for training in beating heart cardiac surgery
Bibliographic record
Abstract
Heart surgeons and trainees benefit greatly from continuous practice of bypass surgery and other cardiac operations. This is true of beating heart surgery where, unlike traditional methods, the heart is not arrested during the operation. At the Dept. of Surgery, University Hospital of the West Indies (UHWI) a system has been devised to simulate a beating human heart using intra-ventricular balloons, which are inserted inside a preserved in vitro porcine heart and made to pulsate using a pneumatic pump. The work is currently being developed in collaboration with the School of Engineering at the University of Technology, Jamaica (UTech) and the Dept. of Mathematics & Computer Science at the University of the West Indies (UWI), with the aim of producing a computer controlled device capable of simulating the range of intra-operative cardiac behaviours typically found in heart surgery. An electromechanical pumping system is described, based on a computer controllable linear actuator. A comparison of associated pulsatile pump choices is presented along with results of the design of a prototype diaphragm pump, which was tested with the porcine heart, demonstrating normal and abnormal beating, and ventricular fibrillation. A software architecture is also presented, showing how the heart may be controlled in a variety of beating modes over the course of a surgical training session.
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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 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".