Pedicle violation and Navigational errors in pedicle screw insertion using the intraoperative O-arm: A preliminary report
Bibliographic record
Abstract
BACKGROUND: Use of computer-assisted insertion of pedicle screws has some advantages owing to the reportedly decreased incidence of pedicle breach and clinical events. Registration-based methods based on preoperative computed tomography imaging, 2D fluoroscopy, and 3D fluoroscopy are the most popular, however each has its limitations. O-arm-based navigation, which uses intraoperative acquisition and registration of navigated images, may overcome many of these disadvantages. We set out to study the clinical accuracy and navigational accuracy for pedicle screw insertion using our recently acquired O-arm and present our preliminary findings. METHODS: The first 26 patients operated consecutively for L4-5 fusion were included in the study. O-arm-based navigation was used to insert the pedicle screws. Postoperative computed tomography images were acquired and assessed for pedicle breach and anterior cortical perforation. Planned trajectories of each screw were compared with the actual trajectories in the postoperative images to assess navigational accuracy in both axial and sagittal planes. RESULTS: A total of 104 screws were inserted. One screw (1%) breached the pedicle laterally. Nonsignificant anterolateral cortical perforations were noted in 7 screws (6.7%), all of which occurred at L5 level. The mean axial and sagittal navigational error was 2.3° (±1.7) and 3.1° (±2.3), respectively. There were no significant differences in the errors between L4 or L5 level. The occurrence of anterior perforation correlated with the degree of axial (P = .02) but not sagittal (P = .12) navigational error. There were no clinical events related to the screw insertion. CONCLUSION: Use of O-arm-guided pedicle screw insertion was associated with low incidence of pedicle breach (1%) and a low range of navigational error in both sagittal and axial planes. Anterolateral vertebral body perforation was higher at L5 without any negative clinical events. Despite the high need for technical support, we found that O-arm was a very efficient tool for accurate pedicle screw insertion.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".