P.174 Accuracy of pedicle screw placement with X-ray versus O-arm image-guided navigation
Bibliographic record
Abstract
Background: Image-guided navigation is routinely used in spine surgery to improve placement of pedicle screws. However, most reports have relied on two-dimensional X-ray evaluation to determine accuracy of screw positioning. In this study, computed tomography (CT) and O-arm imaging enabled a detailed three-dimensional comparison of screw placement. The objective was to compare the accuracy of pedicle screw placement with intraoperative X-ray versus O-arm image-guided navigation. Methods: This was a retrospective analysis of image-guided pedicle screw placement in patients who underwent spinal instrumentation. Post-operative CT and O-arm imaging allowed grading of screw accuracy based on pedicle breaches. Clinical outcomes included patient and operative factors. Results: Pedicle screws were placed in 208 patients (1116 screws). Three-dimensional O-arm guidance was utilized for 126 patients, while the remainder underwent two-dimensional X-ray imaging and post-operative CT assessment. O-arm navigation was associated with improved pedicle screw accuracy: pedicle breaches were more likely to be low grade (odds ratio 2.84, p=0.001) and less likely to be medium grade (odds ratio 0.35, p=0.007) or high grade (odds ratio 0.31, p=0.025). Conclusions: This study provided a detailed comparison of surgical accuracy with X-ray versus O-arm guidance. Navigation with O-arm imaging is associated with benefits in spinal instrumentation, without impacting operative risks for patients.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".