Virtual Reality Compared with Bench-Top Simulation in the Acquisition of Arthroscopic Skill
Bibliographic record
Abstract
BACKGROUND: Work-hour restrictions as set forth by the Accreditation Council for Graduate Medical Education (ACGME) and other governing bodies have forced training programs to seek out new learning tools to accelerate acquisition of both medical skills and knowledge. As a result, competency-based training has become an important part of residency training. The purpose of this study was to directly compare arthroscopic skill acquisition in both high-fidelity and low-fidelity simulator models and to assess skill transfer from either modality to a cadaveric specimen, simulating intraoperative conditions. METHODS: Forty surgical novices (pre-clerkship-level medical students) voluntarily participated in this trial. Baseline demographic data, as well as data on arthroscopic knowledge and skill, were collected prior to training. Subjects were randomized to 5-week independent training sessions on a high-fidelity virtual reality arthroscopic simulator or on a bench-top arthroscopic setup, or to an untrained control group. Post-training, subjects were asked to perform a diagnostic arthroscopy on both simulators and in a simulated intraoperative environment on a cadaveric knee. A more difficult surprise task was also incorporated to evaluate skill transfer. Subjects were evaluated using the Global Rating Scale (GRS), the 14-point arthroscopic checklist, and a timer to determine procedural efficiency (time per task). Secondary outcomes focused on objective measures of virtual reality simulator motion analysis. RESULTS: Trainees on both simulators demonstrated a significant improvement (p < 0.05) in arthroscopic skills compared with baseline scores and untrained controls, both in and ex vivo. The virtual reality simulation group consistently outperformed the bench-top model group in the diagnostic arthroscopy crossover tests and in the simulated cadaveric setup. Furthermore, the virtual reality group demonstrated superior skill transfer in the surprise skill transfer task. CONCLUSIONS: Both high-fidelity and low-fidelity simulation trainings were effective in arthroscopic skill acquisition. High-fidelity virtual reality simulation was superior to bench-top simulation in the acquisition of arthroscopic skills, both in the laboratory and in vivo. Further clinical investigation is needed to interpret the importance of these results.
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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 | 0.004 |
| 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) | 0.003 | 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".