Optimizing Surgical Skills in Cardiac Surgery Residents with Cardiac Transplant in the High-Fidelity Porcine Model
Bibliographic record
Abstract
OBJECTIVES: Simulation is a pivotal tool within cardiac surgery to facilitate learner growth and skill acquisition. There are many methods of simulation and it is possible to develop and implement new curricula incorporating these modalities. The objective of this paper is to describe the feasibility of a high-fidelity cardiac transplant simulation curriculum for surgical residents. METHODS: Our simulation setting was the Animal Resource Center at the University of Calgary. It was set up with 4 separate operating rooms, 2 for donor heart retrievals and 2 for heart implantations. This was done to allow 2 learners to participate with each animal, replicating the true intraoperative environment. Our teaching sessions were facilitated by 2 surgeons experienced in cardiac transplantation. In addition, we had support staff including multiple perfusionists, nurses, and anesthesia technologists. RESULTS: The curriculum was evaluated from many perspectives in real time throughout the simulation as well as afterward in posttest qualitative interviews with all participants. The residents readily identified the acquisition of and increased proficiency in specifically targeted surgical skills. In addition, the residents were able to practice communication, collaboration, and management. Furthermore, the simulation session and our debriefings contributed significantly to fostering a team approach. CONCLUSIONS: The pig is an excellent preclinical model for acquiring and developing the skills necessary for human cardiac transplantation. The residents partaking in the curriculum were satisfied with the learning they received and saw value in the swine transplant curriculum. The overall curriculum was cost-effective, due to the low overall operating costs associated with it.
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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 | 0.004 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".