CT-based Patient-specific Simulation Software for Pedicle Screw Insertion
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Bibliographic record
Abstract
STUDY DESIGN: Development of a 3-dimensional, patient-specific simulator for pedicle screw insertion. OBJECTIVE: To allow the user to practice the insertion of pedicle screws into a 3-dimensional model of a patient-specific spine, and have both visual and quantitative feedback provided to the user. The goal is to better prepare surgeons to perform pedicle screw insertion surgery and help reduce the risk of pedicle screw misplacement. SUMMARY OF BACKGROUND DATA: Pedicle screw insertion is particularly challenging to carry out on patients with abnormal spine morphology. Currently, preoperative planning for pedicle screw insertion is carried out using patient computed tomography and magnetic resonance imaging scans. In addition, once screws are inserted, there are no quantitative metrics against which to measure the results. METHODS: The simulator was developed in the TCL scripting language as a graphical plug-in for the commercial visualization software AmiraDev 3.11. Surgical simulation uses a 3-dimensional model of patient's spine developed from the patient's computed tomography scan. RESULTS: Pedicle screw insertion can be practiced using pedicle screws of various sizes and analyzed in both 2-dimension and 3-dimension. Quantitative feedback is provided to the user in the form of anatomic lengths and angles, relative purchase of inserted screws, and a screw placement grading system. The software allows the user to adjust the translucency of a patient's spine to develop a better sense of the trajectories and depths involved with performing pedicle screw insertion on a patient. CONCLUSIONS: The simulator offers many helpful features to the surgeon with respect to complex cases and to the surgical trainee learning the basic technique of pedicle screw insertion. A study is currently underway to evaluate the efficacy of the simulator as a teaching tool for surgical trainees in placing pedicle screws. Future work will focus on the transfer of the software to a stand-alone platform.
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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) | 0.000 | 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 it