Pedicle Screw Malposition in Revision Spinal Surgery: Efficacy of Intraoperative CT-Based Navigation
Bibliographic record
Abstract
Introduction The improved screw accuracy of intraoperative 3D imaging with navigation has been primarily reported in cadaveric studies and spinal deformity surgeries. Revision surgery poses unique technical challenges for pedicle screw instrumentation because of an established fusion mass, lack of reliable anatomical landmarks, and limited fixation options. The aim of this study was to examine our early experience of pedicle screw malposition rates in revision surgeries comparing O-arm and StealthStation navigated cases to the traditional freehand technique and evaluate the impact on patient outcome. Materials and Methods This ambispective study compared two matched cohorts of patients undergoing revision thoracolumbar surgery. The study group comprised 56 consecutive patients who underwent O-arm and StealthStation navigation-assisted pedicle screw instrumentation (NAV) between January 1, 2008 and December 31, 2012. The control group comprised 34 historical matched cases with surgery between January 1, 2006 and December 31, 2008, using traditional (freehand or with fluoroscopy), nonnavigated techniques (non-NAV). Cases were matched on age, gender, surgical location, number of surgical levels, and primary diagnosis. We examined the differences in screw placement and accuracy, patient outcomes, and surgery-related adverse events. Results There was a significant difference in the number of misplaced screws between the NAV and non-NAV groups (31 vs. 54, p < 0.001). The average number of misplaced screws per case was 0.57 (SD = 0.92) with navigation and 1.86 (SD = 2.49) without ( p = 0.01). No difference existed in the number of screws revised intraoperatively (10 vs. 7, p = 0.54). One patient in the NAV group and two patients in the non-NAV group required early postoperative screw revision during the same admission ( p = 0.33). No screws in the NAV group required revision during subsequent admissions. No difference was observed in grade of screw malposition ( p = 0.11) or anatomical location of malposition ( p = 0.26). Anatomically, a majority of misplaced screws were located below T6 in both groups. The use of navigation resulted in a significant difference in intraoperative massive blood loss (> 2L in 3 hours), 3.3% of the NAV cases compared with 7.8% of the non-NAV cases. There was no significant difference in the duration of surgery ( p = 0.11), incidence of intraoperative dural tear ( p = 1.00), wound infection ( p = 1.00), or length of stay ( p = 0.78). Conclusion This early analysis of our experience in revision surgery demonstrates an increased accuracy of pedicle screw placement utilizing O-arm imaging and StealthStation navigation systems, without an increase in OR time. Small case numbers likely explain the comparable early screw revision rates, as our experience with a larger number of degenerative cases demonstrates a statistically significant reduction in reoperation for symptomatic screw misplacement. The use of intraoperative 3D imaging and navigation in revision spine surgeries results in lower mean blood loss, and fewer cases of massive intraoperative blood loss may occur because navigation avoids the extent of dissection normally required and facilitates safe pedicle screw instrumentation while minimizing surgical exposure. Data collection and analysis is ongoing to examine diagnosis-specific incidence of screw malposition and patient outcome.
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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 | 0.010 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".