Bibliographic record
Abstract
INTRODUCTION: Use of virtual reality (VR) simulators in endovascular interventional education has become increasingly popular yet many questions surrounding this nascent technology remain unanswered. While progress has been made in other disciplines such as endoscopy and minimally invasive surgery, scientific evidence investigating endovascular simulations remains limited. The general aim of this dissertation was to conduct validation studies to elucidate the potential for skills acquisition and assessment outside of the catheterization laboratory using VR simulation. Endovascular skills transfer from VR-Lab to the porcine laboratory (P-Lab) was also investigated. An economic analysis was performed to assist in the establishment of a realistic VR implementation strategy.\nMATERIALS AND METHODS: Simulator validations were conducted by comparing performance metrics collected from novices and experienced physicians using Student’s t-test. Performance metrics were recorded by the simulator while participants treated simulated patients suffering from renal artery stenosis (RAS) and carotid artery stenosis (CAS). Endovascular skills transfer was tested using the P-Lab as an approximation of the human catheterization laboratory. A group of endovascular novices were evaluated in the P-Lab and the VR-Lab using an objective skills assessment of technical skills (OSATS), yielding a Total Score. Participants were then randomized into different training groups, put through their assigned training schema and subsequently re-evaluated in both laboratories. ANCOVA analysis was conducted to compare the cumulative effect each type of training had on Total Score. Consumable and rental fees from the skills transfer study were used to calculate the comparison data for the economical analysis.\nRESULTS: Face validity was demonstrated for both the renal and carotid artery stenosis modules. Neither construct validity study produced results which differentiated between the expert and novice performance metrics except for fluoroscopic and procedural times. VR-Lab training sessions generated skills which improved P-Lab performances. VR-Lab training cost less than the P-Lab using our economical analysis.\nCONCLUSIONS: Despite demonstrating face validity, VR-Lab simulations should not be used alone for skills assessment outside of the catheterization laboratory in its present form. Skills learned in virtual reality transfer favorably to the P-Lab and simulation training seems to offer a viable alternative of non-clinical training. The VR-Lab affords a more economical method to teach and practice endovascular skills compared to the P-lab. Further research is needed to elucidate the relative efficacies of both training methods
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".