Training and assessment of technical skills and competency in cardiac surgery
Bibliographic record
Abstract
The assessment of surgical competency has become a priority for both surgical educators and licensing boards. Surgical educators must incorporate rigorous, reliable, and valid means of assessment into residency programs. Objective evaluation of technical skills has been extensively explored in various surgical specialties, but its role in cardiac surgery has not been well studied and there is limited experience with integration into the educational curricula. Several cardiac and vascular surgery simulation models have been designed and evaluated, ranging from simple low-fidelity models using inert materials to a complex, computer-controlled, high-fidelity simulator using biological tissues to practice entire surgical cases. Most of the available models have not been well validated or integrated into educational curricula. The cardiac surgery simulation tools in development need validation and incorporation into structured, competency-based training curricula. The ongoing development of surgical simulators and educational curricula will enable a transition from the century-old graded responsibility training program to a competency-based program, where trainees must demonstrate technical competence to progress to the next level of training and gain certification and re-certification--ultimately ensuring better and faster technical skill acquisition as well as improved quality of care and patient safety.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.018 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".