Simulation reduces navigational errors in cerebral angiography training
Bibliographic record
Abstract
Abstract Background Simulation-based medical education (SBME) is growing as a powerful aid in delivering proficient skills training in many specialties. Cerebral angiography (CA), a spatially and navigationally challenging endovascular procedure, can benefit from SBME by training targetable skills outside of the Angiosuite. In order to standardize and specify training requirements, navigational challenges and needs have to be identified. Furthermore, to enable successful adoption of these strategies, simulation adoption barriers, such as necessity of supervisory resources, must be reduced. In this study, we assessed the navigational challenges in simulated CA through a self-guided novice training program. Methods Novice participants ( n = 14) received virtual reality (ANGIO Mentor, Simbionix) diagnostic cerebral angiography training and were tested on a right middle cerebral artery aneurysm case over 8 sessions with a reference instructional outline. The navigational trajectories for the guidewire and catheter were analyzed and rates in erroneous vessel access were analyzed. Participants were given a Mental Rotations Test (MRT) and were analyzed based on MRT performance. Results After 8 sessions, there was a significant ( p < 0.05) reduction on navigational error prevalence. The L-SUB and L-CCA saw the biggest drop in erroneous access, whereas the R-ECA, the biggest consumer of error time, saw no changes in access frequency. Individuals with high MRT score performed much better ( p < 0.05) than those with low MRT score. Conclusions Through self-guided simulation training, we demonstrated the navigational challenges encountered in simulated CA. To establish better assessments and standards in medical training, we can create self-guided training curricula aimed at correcting errors, enabling repetitive practice, and reducing human resource needs.
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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) | 0.002 | 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".