Accuracy of Intraoperative Computed Tomography–Based Navigation for Placement of Percutaneous Pedicle Screws
Bibliographic record
Abstract
MISS techniques have gained recent popularity. The proposed benefits of these techniques include reduced tissue trauma, reduced blood loss, less perioperative pain, and a quicker recovery and return to normal activities. The purpose of this study was to evaluate the accuracy of intraoperative computed tomography (CT)-based navigation for placement of percutaneous pedicle screws in a cadaveric model. Outcome measures included accuracy of screw placement. Two cadaveric specimens were utilized. CT images were obtained using an O-Arm (Medtronic, Memphis, Tennessee, United States) and were coupled to the Stealth navigation system (Medtronic). Computer navigation was used for placement of percutaneous pedicle screws. Screws were placed bilaterally from T5 to S1. Postinsertion CT scans were obtained. Pedicle breach was assessed and classified (I: none, II: < 2 mm, III: 2 to 4 mm, or IV: > 4 mm) with direction of breach. Thirty thoracic screws were placed with 3 (10%) medial breaches and 17 (56.7%) lateral breaches (grade III). Of 20 lumbar screws there were 0 medial breaches and 2 (10%) lateral breaches (1 grade III, 1 grade IV). Four sacral screws were placed without breaches. The real-time computer-aided navigation tool ("simulated screw") was limited in identifying a breach. Manipulation of the surgeon's hand or driver could change the orientation of the navigation tool without changing the screw trajectory. CT-based navigation for percutaneous pedicle screw placement appears safe for the lumbar spine. Lateral thoracic breaches appeared commonly but were not felt to be clinically significant. The 10% rate of medial thoracic breach was concerning, but definitive conclusions could not be made due to the small sample size.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".