Teaching heart valve surgery techniques using simulators: a systematic review
Bibliographic record
Abstract
The apprentice model has traditionally been the primary method of teaching cardiac surgery trainees. Limitations of this model include insufficient time to learn all necessary skills, minimal exposure to rare cases and to complex repair techniques, small number of patients in small centres, high cost and absence of objective measures of feedback. In recent years, simulation-based training (SBT) has been used in order to address the gaps left by the apprentice model. We performed a systematic review of PubMed and Embase for articles investigating the use of SBT in teaching surgical valve techniques published in 2022 or earlier in order to summarize the current literature regarding the use of SBT for trainees learning surgical valve repair and replacement techniques. We compiled data on the impact of SBT on time to completion of tasks, proportion of trainees who committed technical errors, skills scores and theoretical knowledge. Studies in which outcomes were evaluated showed significant improvement in these measures after participation in SBT. Simulation-based training has been shown to improve the surgical skills of trainees in a rela-tively short period. As hands-on experience in the field of cardiac surgery is invaluable and often difficult to reproduce effectively, it is likely that a combination of hands-on training and SBT will be adopted moving forward to provide optimal exposure for surgical trainees.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".