Virtual reality in spinal endoscopy: a paradigm shift in education to support spine surgeons
Bibliographic record
Abstract
BACKGROUND: Minimally invasive spine surgery (MISS) and endoscopic spine surgery have continually evolving indications in the cervical, thoracic, and lumbar spine. Endoscopic spine surgery entails treatment of disc disease, stenosis, spondylolisthesis, radiculopathy, and deformity. MISS involves complex motor skills in regions of variable anatomy. Simulator use has been proposed to aid in training and skill retention, preoperative planning, and intraoperative use. METHODS: A systematic review of five databases was performed for publications pertaining to the use of virtual (VR), augmented (AR), and mixed (MR) reality in MISS and spinal endoscopic surgery. Qualitative data analysis was undertaken with focus of study design, quality, and reported outcomes. Study quality was assessed using the Medical Education Research Quality Instrument (MERSQI) score and level of evidence (LoE) by a modified Oxford Centre for Evidence-Based Medicine (OCEBM) level for simulation in medicine. RESULTS: Thirty-eight studies were retained for data collection. Studies were of intervention-control, clinical application, and pilot or cross-sectional design. Identified articles illustrated use of VR, AR, and MR in all study designs. Procedures included pedicle cannulation and screw insertion, vertebroplasty, kyphoplasty, percutaneous transforaminal endoscopic discectomy (PTED), lumbar puncture and facet injection, transvertebral anterior cervical foraminotomy (TVACF) and posterior cervical laminoforaminotomy. Overall MERSQI score was low-to-medium [M =9.71 (SD =2.60); range, 4.5-13.5], and LoE was predominantly low given the number of purely descriptive articles, or low-quality randomized studies. CONCLUSIONS: The current scope of VR, AR, and MR surgical simulators in MISS and spinal endoscopic surgery was described. Studies demonstrate improvement in technical skill and patient outcomes in short term follow-up. Despite this, overall study quality and levels of evidence remain low. Cohesive study design and reporting with focus on transfer validity in training scenarios, and patient derived outcome measures in clinical studies are required to further advance the field.
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 | 0.026 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| 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".