Mentored Simulation Training Improves Procedural Skills in Cardiac Catheterization
Bibliographic record
Abstract
BACKGROUND: Despite valuable supplemental training resources for surgical skill acquisition, utility of virtual reality simulators to improve skills relevant to performing cardiac catheterization has not been evaluated. METHODS AND RESULTS: Post baseline cardiac catheterization performance assessment, 27 cardiology trainees were randomized to either mentored training on a virtual reality simulator (n=12) or no simulator training (control; n=15). Cardiac catheterization performance was reassessed 1 week post baseline assessment. Performance scores at 1 week were compared with baseline within each group, and change in score from baseline to 1 week was compared between groups. Linear regression modeling was performed to assess the effect of simulator training as a function of baseline performance. Technical performance improved postintervention in the simulator group (24 versus 18; P=0.008) and changed marginally in the control group (20 versus 18; P=0.054). Improvement in technical performance was greater in the simulator group (6 versus 1; P=0.04). Global performance improved postintervention in both groups (simulator, 24 versus 17, P=0.01; control, 20 versus 18, P=0.02), with a trend toward greater improvement in the simulator group (5 versus 2; P=0.11). Lower scores at baseline were associated with larger differences in postintervention scores between the simulator and control groups (technical performance, P=0.0006; global performance, P<0.0001). CONCLUSIONS: Skills required to perform cardiac catheterization can be learned via mentored simulation training and are transferable to actual procedures in the catheterization laboratory. Less proficient operators derive greater benefit from simulator training than more proficient operators.
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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 | 0.000 | 0.003 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".