Fully Immersive Virtual Reality for Total Hip Arthroplasty
Bibliographic record
Abstract
BACKGROUND: Fully immersive virtual reality (VR) uses headsets to situate a surgeon in a virtual operating room to perform open surgical procedures. The aims of this study were to determine (1) if a VR curriculum for training residents to perform anterior approach total hip replacement (AA-THR) was feasible, (2) if VR enabled residents' performance to be measured objectively, and (3) if cognitive and motor skills that were learned with use of VR were transferred to the physical world. METHODS: The performance of 32 orthopaedic residents (surgical postgraduate years [PGY]-1 through 4) with no prior experience with AA-THR was measured during 5 consecutive VR training and assessment sessions. Outcome measures were related to procedural sequence, efficiency of movement, duration of surgery, and visuospatial precision in acetabular component positioning and femoral neck osteotomy, and were compared with the performance of 4 expert hip surgeons to establish competency-based criteria. Pretraining and post-training assessments on dry bone models were used to assess the transfer of visuospatial skills from VR to the physical world. RESULTS: Residents progressively developed surgical skills in VR on a learning curve through repeated practice, plateauing, on average, after 4 sessions (4.1 ± 0.6 hours); they reached expert VR levels for 9 of 10 metrics (except femoral osteotomy angle). Procedural errors were reduced by 79%, assistive prompts were reduced by 70%, and procedural duration was reduced by 28%. Dominant and nondominant hand movements were reduced by 35% and 36%, respectively, and head movement was reduced by 44%. Femoral osteotomy was performed more accurately, and acetabular implant orientation improved in VR assessments. In the physical world assessments, experts were more accurate than residents prior to simulation, but were matched by residents after simulation for all of the metrics except femoral osteotomy angle. The residents who performed best in VR were the most accurate in the physical world, while 2 residents were unable to achieve competence despite sustained practice. CONCLUSIONS: For novice surgeons learning AA-THR skills, fully immersive VR technology can objectively measure progress in the acquisition of surgical skills as measured by procedural sequence, efficiency of movement, and visuospatial accuracy. Skills learned in this environment are transferred to the physical environment.
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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 | 0.002 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".