Longitudinal Evaluation of Congenital Cardiovascular Surgical Performance and Skills Retention Using Silicone-Molded Heart Models
Bibliographic record
Abstract
Objective: Hands-on surgical training (HOST) for congenital heart surgery (CHS), utilizing silicone-molded models created from 3D-printing of patients’ imaging data, was shown to improve surgical skills. However, the impact of repetition and frequency of repetition in retaining skills has not been previously investigated. We aimed to longitudinally evaluate the outcome for HOST on two example procedures of different technical difficulties with repeated attempts over a 15-week period. Methods: Five CHS trainees were prospectively recruited. Repair of coarctation of the aorta (CoA) and arterial switch operation (ASO) were selected as example procedures of relatively low and high technical difficulty. Procedural time and technical performance (using procedure-specific assessment tools by the participant, a peer-reviewer, and the proctor) were measured. Results: Coarctation repair performance scores improved after the first repetition but remained unchanged at the follow-up session. Likewise, CoA procedural time showed an early reduction but then remained stable (mean [standard deviation]: 29[14] vs 25[15] vs 23[9] min at 0, 1, and 4 weeks). Conversely, ASO performance scores improved during the first repetitions, but decreased after a longer time delay (>9 weeks). Arterial switch operation procedural time showed modest improvements across simulations but significantly reduced from the first to the last attempt: 119[20] versus 106[28] min at 0 and 15 weeks, P = .049. Conclusions: Complex procedures require multiple HOST repetitions, without excessive time delay to maintain long-term skills improvement. Conversely, a single session may be planned for simple procedures to achieve satisfactory medium-term results. Importantly, a consistent reduction in procedural times was recorded, supporting increased surgical efficiency.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".