<b>Assessment of a Virtual Interventional Simulator Trainer</b>
Bibliographic record
Abstract
PURPOSE: To assess the construct validity of the Procedicus Virtual Interventional Simulator Trainer (Procedicus-VIST) and its use as a training tool. METHODS: Two groups comprised of 8 interventional radiologists (experts) and 8 medical students (novices) performed 6 renal artery procedures on the Procedicus-VIST. All participants received a 45-minute standardized didactic introduction before starting the simulations. The first 2-hour session was used for familiarization, whereas the second session constituted the testing period. During each procedure, objective performance data including procedure time, fluoroscopic time, contrast, cine loops, lesion coverage, tool:lesion ratio, placement accuracy, and residual stenosis were recorded by the Procedicus-VIST software. Exit surveys were completed to document demographic and subjective data. A visual analogue scale (VAS) from 0 to 100 was used to rate total, guidewire, catheter, balloon, stent, fluoroscopic, and joystick realism, as well as the simulator's pedagogic value. RESULTS: There were no significant differences in performances between the 2 groups in residual stenosis, placement accuracy, procedure time, number of cine loops, lesion coverage, or tool:lesion ratio. The total fluoroscopic use was greater for the novice group (p < 0.01). Experts rated 6 of the 8 subjective parameters favorably, whereas the novice group approved of 7. CONCLUSIONS: Using this study design, the quantitative metrics recorded by the Procedicus-VIST software failed to stratify performances based upon experience level, with the exception of fluoroscopic use. Investigation comparing standard training to virtual reality training should be performed to assess any differences in actual performance in the catheterization laboratory.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".