Machine-vision image guided C4-C5 unilateral cervical pedicle screw insertion: case report and review of literature
Bibliographic record
Abstract
Computer-assistance for pedicle screw insertion is becoming increasingly common. As in the case presented below, image guided neuronavigation can be used to improve the accuracy and safety of subaxial cervical pedicle screw placement, given their increased difficulty of cannulation relative to the larger pedicles in the thoracolumbar spine. A 49-year-old man presented with a traumatic sagittal split fracture of C4 (AO Classification type A4) and a left lateral mass fracture of C5 (AO Classification type F1) with anterior depression and 50% height loss. MRI revealed evidence of strain of the interspinous/supraspinous ligaments (AO Classification type B2), as well as fluid within the left C4-C5 facet joint. Along with these fractures, the young patient had Klippel-Feil syndrome with autofusion of the C2-3 posterior elements, and a left vertebral artery dissection. He had neck pain but was otherwise neurologically intact. The patient underwent machine-vision image guided C4-C5 unilateral pedicle screw insertion connected by a fixation rod for stabilization and bone graft to augment the instrumented fusion with good outcome. The use of machine vision spinal navigation was able to provide accurate and precise placement of pedicle screws without significantly increasing surgical time. This method has the potential to allow for the safe and accurate insertion of cervical pedicle screws and to reduce the rate of pedicle breach which could avoid significant neurovascular injury.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".