Evaluating the role of endovascular simulation training in enhancing surgical performance metrics and patient outcomes in vascular surgery: A scoping review
Bibliographic record
Abstract
Objective To assess the effectiveness of endovascular simulation training in enhancing surgical performance metrics and its influence on patient outcomes in vascular surgery. Methods A scoping review was conducted to explore the impact of simulation-based training in vascular surgery, with a specific focus on procedural metrics such as fluoroscopy time, radiation exposure, and contrast volume, as well as patient outcomes including perioperative complications, morbidity, and mortality. Comprehensive searches of Scopus, OVID Medline, and OVID Embase were performed using structured query strings encompassing terms related to endovascular simulation, training methods, and measurable clinical and procedural outcomes. Screening and selection adhered to the PRISMA-ScR guidelines. Studies were included if they assessed simulation-based training for endovascular procedures and reported measurable technical and patient-centered outcomes; reviews, commentaries, and studies not involving endovascular simulation or relevant metrics were excluded. Data were extracted on study characteristics, simulation modalities, clinical endpoints, and procedural performance metrics, and the findings were synthesized to identify trends in the literature. Results Six studies met the inclusion criteria, utilizing a variety of simulation modalities, including virtual reality, 3D-printed models, and patient-specific rehearsal. Simulation training was associated with significant improvements in procedural metrics during real and simulated procedures, including reductions in fluoroscopy time, procedure duration, radiation exposure, and contrast volume. Improvements in technical proficiency and operator confidence were consistently observed across studies. However, the evidence linking simulation to direct patient-specific outcomes, such as reduced perioperative complications or mortality, was limited. While two studies demonstrated statistically significant improvements in clinical outcomes, others showed trends without statistical significance, and two studies found no measurable impact on patient outcomes. Conclusions Simulation-based training enhances procedural efficiency, technical performance, and operator confidence in vascular surgery. However, direct evidence linking simulation training to improved patient outcomes remains inconclusive. Future research should focus on high-quality, multicenter randomized controlled trials with standardized outcome measures to better establish the clinical value of simulation training and inform its widespread integration into vascular surgery education and training programs.
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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 | 0.045 | 0.193 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.026 | 0.021 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".