Effect of Simulation‐Based Cerebral Angiography Training on Navigational Error in Novices
Bibliographic record
Abstract
Endovascular surgical procedures require visual‐spatial coordination in workspaces with restricted motions and temporally limited imaging. The development of the skills needed for these procedures can be facilitated by 3D simulator‐based training. Simulation‐based medical education has recently started focusing on personalized training in reducing errors, enhancing trans‐situational competence and promoting professional transparency. Cerebral angiography (CA) has lagged behind in this training approach due to the lack of validated, realistic training models, relying strictly on clinical case exposure frequency as a means of assessing proficiency. The ANGIO Mentor visual‐haptic simulator has been regarded as an effective training tool, increasing performance in diagnostic CA, however, this simulator has not been tested thoroughly in error reduction in CA. In our study, residents and graduate students were given practice diagnostic angiography and were subsequently tested on a right middle cerebral artery aneurysm case, repeating over 8 sessions. Participants were also administered a mental rotations test (MRT) and grouped into MRT groups to identify performance differences. We have identified a significant self‐guided reduction in spatial errors by the participants over 8 sessions. Further investigation revealed a negative change in error frequency for major trajectory deviations. This allowed us to categorize vessels that, although less frequent in erroneous access, were creating the most difficulty for the trainee and most potential harm to patients. Assessing MRTs, we found that high MRT individuals performed much better than low MRT individuals at the start of the study, however, both groups plateaued at a similar performance level by the 8th session. These results are significant in the adoption of personalized medical training in this field and identify vascular areas of difficulty that can be addressed by the curriculum. Although it is well understood that simulation training under expert supervision is most effective, we have shown that self‐guided training can build appropriate technical and spatial ability in procedural skill development. Support or Funding Information NSERC
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".