Virtual reality as a learning tool in spinal anatomy and surgical techniques
Bibliographic record
Abstract
Background Surgical simulation is a valuable educational tool for trainees to practice in a safe, standardized, and controlled environment. Interactive feedback-based virtual reality (VR) has recently moved to the forefront of spine surgery training, with most commercial products focusing on instrumentation. There is a paucity of learning tools directed at decompression principles. The purpose of this study was to evaluate the efficacy of VR simulation and its educational role in learning spinal anatomy and decompressive techniques. Methods A VR simulation module was created with custom-developed software. Orthopaedic and neurosurgical trainees were prospectively enrolled and interacted with patient-specific 3D models of lumbar spinal stenosis while wearing a headset. A surgical toolkit allowed users to perform surgical decompression, specifically removing soft tissues and bone. The module allowed users to perform various techniques in posterior decompressions and comprehend anatomic areas of stenosis. Pre- and post-module testing, and utility questionnaires were administered to provide both quantitative and qualitative evaluation of the module as a learning device. Results 28 trainees were enrolled (20-orthopaedic, 8-neurosurgery) in the study. Pre-test scores on anatomic knowledge progressively improved and showed strong positive correlation with year-in-training (Pearson's r = 0.79). Following simulation, the average improvement in post-test scores was 11.4% in junior trainees (PGYI-III), and 1.0% in senior trainees (PGYIII-Fellows). Knowledge improvement approached statistical significance amongst junior trainees ( p = 0.0542). 89% of participants found the VR module useful in understanding and learning the pathology of spinal stenosis. 71% found it useful in comprehending decompressive techniques. 96% believed it had utility in preoperative planning with patient-specific models. Conclusions Our original VR spinal decompression simulation has shown to be overwhelmingly positively received amongst trainees as both a learning module of patho-anatomy and patient-specific preoperative planning, with particular benefit for junior trainees.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".