The Use of Intraoperative CT and Navigation for the Treatment of Spinal Deformity in Open and Minimally Invasive Surgery
Bibliographic record
Abstract
Recent advances in the use of in-operating room computed tomography (CT) scanning have enhanced the usability of navigation in spinal surgery. Presurgical advanced imaging studies had limited accuracy because of changes in spinal position from supine to prone, making early efforts at spinal navigation cumbersome and inaccurate. In-room scanning with patient-based markers has enhanced the accuracy of navigated spinal instrumentation and of robot-guided spinal instrumentation. Both may be used in open and minimally invasive complex spinal deformity and revision surgeries to enhance the accuracy of spinal instrumentation and to decrease reoperation due to screw malposition. Intraoperative navigation can result in net cost savings to a hospital system and increased radiation safety for hospital personnel. The accuracy of pedicle screw placement depends on the experience of the surgeon and the method used. Published rates of accuracy hold thoracolumbar freehand screw placement at 72% to 94%.1 Use of fluoroscopy may increase accuracy to 84% to 94%.2 It is important to note that not all malpositioned pedicle screws require revision surgery, and published rates of revision range from 2% to 8%.3 Even with such low rates, such revisions, on average, cost the health care system $23,762.3 In the aggregate, such costs are undesirable, as are potential complications such as dural tear or neurologic injury. Busy spine centers may consider the utility of intraoperative CT scanning for screw checking or three-dimensional navigation to improve the accuracy of spinal instrumentation. Intraoperative CT scanning and associated navigation tools have evolved over the past 10 years with improved accuracy. In part, acceptance of navigation has lagged as the result of work flow in the operating room and time constraints. Additionally, intraoperative CT scanning in the prone position has enhanced the accuracy of navigation over preoperative scans, which are obtained supine. Modern intraoperative navigation and robot-assisted navigation can enhance surgical accuracy and improve the safety profile of complex spinal surgery (Figure 1).Figure 1: Navigation in minimally invasive spine surgery. Marker placement at T10 for percutaneous L1-pelvis surgery.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".