Intraoperative Error Propagation in 3-Dimensional Spinal Navigation From Nonsegmental Registration: A Prospective Cadaveric and Clinical Study
Bibliographic record
Abstract
Study Design: Prospective pre-clinical and clinical cohort study. Objectives: Current spinal navigation systems rely on a dynamic reference frame (DRF) for image-to-patient registration and tool tracking. Working distant to a DRF may generate inaccuracy. Here we quantitate predictors of navigation error as a function of distance from the registered vertebral level, and from intersegmental mobility due to surgical manipulation and patient respiration. Methods: Navigation errors from working distant to the registered level, and from surgical manipulation, were quantified in 4 human cadavers. The 3-dimensional (3D) position of a tracked tool tip at 0 to 5 levels from the DRF, and during targeting of pedicle screw tracts, was captured in real-time by an optical navigation system. Respiration-induced vertebral motion was quantified from 10 clinical cases of open posterior instrumentation. The 3D position of a custom spinous-process clamp was tracked over 12 respiratory cycles. Results: An increase in mean 3D navigation error of ≥2 mm was observed at ≥2 levels from the DRF in the cervical and lumbar spine. Mean ± SD displacement due to surgical manipulation was 1.55 ± 1.13 mm in 3D across all levels, ≥2 mm in 17.4%, 19.2%, and 38.5% of levels in the cervical, thoracic, and lumbar spine, respectively. Mean ± SD respiration-induced 3D motion was 1.96 ± 1.32 mm, greatest in the lower thoracic spine ( P < .001). Tidal volume and positive end-expiratory pressure correlated positively with increased vertebral displacement. Conclusions: Vertebral motion is unaccounted for during image-guided surgery when performed at levels distant from the DRF. Navigating instrumentation within 2 levels of the DRF likely minimizes the risk of navigation error.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".