Utilization of Spinal Intra-operative Three-dimensional Navigation by Canadian Surgeons and Trainees: A Population-based Time Trend Study
Bibliographic record
Abstract
BACKGROUND: Computer-assisted navigation (CAN) improves the accuracy of spinal instrumentation in vertebral fractures and degenerative spine disease; however, it is not widely adopted because of lack of training, high capital costs, workflow hindrances, and accuracy concerns. We characterize shifts in the use of spinal CAN over time and across disciplines in a single-payer health system, and assess the impact of intra-operative CAN on trainee proficiency across Canada. METHODS: A prospectively maintained Ontario database of patients undergoing spinal instrumentation from 2005 to 2014 was reviewed retrospectively. Data were collected on treated pathology, spine region, surgical approach, institution type, and surgeon specialty. Trainee proficiency with CAN was assessed using an electronic questionnaire distributed across 15 Canadian orthopedic surgical and neurosurgical programs. RESULTS: In our provincial cohort, 16.8% of instrumented fusions were CAN-guided. Navigation was used more frequently in academic institutions (15.9% vs. 12.3%, p<0.001) and by neurosurgeons than orthopedic surgeons (21.0% vs. 12.4%, p<0.001). Of residents and fellows 34.1% were fully comfortable using spinal CAN, greater for neurosurgical than orthopedic surgical trainees (48.1% vs. 11.8%, p=0.008). The use of CAN increased self-reported proficiency in thoracic instrumentation for all trainees by 11.0% (p=0.036), and in atlantoaxial instrumentation for orthopedic trainees by 18.0% (p=0.014). CONCLUSIONS: Spinal CAN is used most frequently by neurosurgeons and in academic centers. Most spine surgical trainees are not fully comfortable with the use of CAN, but report an increase in technical comfort with CAN guidance particularly for thoracic instrumentation. Increased education in spinal CAN for trainees, particularly at the fellowship stage and, specifically, for orthopedic surgery, may improve adoption.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".