The use of virtual reality for training in carotid artery stenting: a construct validation study
Bibliographic record
Abstract
BACKGROUND: Given that carotid artery stenosis (CAS) intervention is procedurally difficult, possesses an extensive learning curve, and involves a grave list of potential complications, construct validation of new non-clinical training devices is of increasing importance. PURPOSE: To evaluate the construct validity of the Procedicus-Virtual Interventional Simulator Trainer (Procedicus-VIST) and its use as a training tool. MATERIAL AND METHODS: Sixteen interventionalists (15 males, one female; mean interventional radiology [IR] experience >11 years) and 16 medical students (15 males, one female; no IR experience) received 1 hour of didactic instruction followed by an hour of familiarization training. Subjects then attempted to complete a carotid artery stenting procedure within 1 hour while their performance metrics were recorded. All participants completed a qualitative exit survey of subjective parameters using a visual analog scale. RESULTS: Procedure and fluoroscopic time was 8.7 and 8.7 min greater in the novice group (P=0.0066 and P=0.0031), respectively. There were no significant differences in performances between the two groups in the remaining metrics of cine loops (number recorded), tool/vessel ratio, coverage percentage, and placement accuracy or residual stenosis. Contrast measurement metrics were found to be too imprecise for statistical analysis. Experienced and novice opinions differed significantly for six of 10 subjective parameters. No statistically significant difference in video-gaming habits was demonstrated. CONCLUSION: With the exception of the metrics of performance time and fluoroscopic use, construct validity of the Procedicus-VIST carotid metrics were not confirmed. Virtual reality simulation as a training method was valued more by novices than by experienced interventionalists.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".