Clinical translation of surgical simulated closure of a ventricular septum defect
Bibliographic record
Abstract
OBJECTIVES: To demonstrate that improvement in technical performance of congenital heart surgical trainees during ventricular septum defect (VSD) closure simulation translates to better patient outcomes. METHODS: Seven trainees were divided into 2 groups. Experienced-fellows group included 4 senior trainees who had performed >5 VSD closures. Residents group consisted of 3 residents who had never performed a VSD closure. Experienced-fellows completed 3 VSD closures on real patients as a pretest. Both groups participated in a 4-week simulation requiring each participant to complete 2 VSD closures on three-dimensional printed models per week. One month later, all trainees returned for a post-test operation in real patients. All performances were recorded, blinded and scored independently by 2 cardiac surgeons using the validated Hands-On Surgical Training-Congenital Heart Surgery (HOST-CHS). Predefined surgical outcomes were analysed. RESULTS: The median HOST-CHS score increased significantly from week 1 to 4 [50 (39, 58) vs 73 (65, 74), P < 0.001] during simulation. The improvement in the simulation of experienced-fellows successfully transferred to skill acquisition [HOST-CHS score 72.5 (71, 74) vs 54 (51, 60), P < 0.001], with better patients outcomes including shorter total cross-clamp time [pretest: 86 (70, 99) vs post-test: 60 (53, 64) min, P = 0.006] and reduced incidence of major patch leak requiring multiple pump runs [pretest: 4/11 vs post-test: 0/9, P = 0.043]. After simulation, the technical performance and surgical outcomes of Residents were comparable to Experienced-fellows in real patients, except for significantly longer cross-clamp time [Residents: 76.5 (71.7, 86.8) vs Experienced-fellows: 60 (53, 64) min, P = 0.002]. CONCLUSIONS: Deliberate practice using simulation translates to better performance and surgical outcomes in real patients. Residents who had never completed a VSD closure could perform the procedures just as safely and effectively as their senior colleagues following simulation.
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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 | 0.001 | 0.006 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".