Assessment of a Novel, Adjustable Task Trainer for Cardiac Surgical Skills
Bibliographic record
Abstract
INTRODUCTION: A recent needs assessment in Canadian cardiac surgery programs identified the desire for a coronary artery bypass (CABG) and aortic valve replacement (AVR) simulation model for home practice. We aimed to develop and assess a portable, adjustable task trainer for cardiac surgical skills with high functional task alignment. METHODS: Intraoperative measurements were taken from patients undergoing elective CABG and AVR (N = 30). Measurements were taken in 3 axes and used to create a chest cavity that resembles the mediastinal constraints of a patient undergoing CABG and AVR. The task trainer is adjustable on the following 3 levels: (1) size of the incision, (2) depth of the chest, and (3) relative position of coronary artery or aortic valve model within the chest. Three groups (novices, intermediates, and experts) of cardiac surgery members evaluated the task trainer for functional task alignment and construct validity. RESULTS: The CABG and AVR model had high functional task alignment. There was a high satisfaction for both models and all participants would recommend the AVR and CABG model as an educational tool. Performance time significantly differed between the groups for both models (CABG: P = 0.032 and AVR: P = 0.001), as well as number of errors (CABG: P = 0.04 and AVR: P = 0.043). CONCLUSIONS: Using real patient data, we were able to develop an adjustable task trainer for training principles of CABG and AVR. Our pilot study provides preliminary sources of evidence for validity and future study will look to assess transferability of skill to the operating room.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".