Toward the next generation of simulator for intraoperative navigation of scoliotic spine surgeries
Bibliographic record
Abstract
Introduction: Surgical navigation systems are useful for planning pedicle screw positioning and guiding drilling trajectories. However, it is not yet possible to intraoperatively predict the correction of the scoliotic spine resulting from a specific screws and rods configuration and instrumentation maneuvers. Objective: To develop a novel intraoperative simulator for navigated scoliotic spine surgeries. Methods: An instrumentation strategy (pedicle screw insertion, rod attachment and rotation, set screw tightening) was computationally simulated on a synthetic model of a scoliotic spine using the preoperative radiographs in the standing position and various parameters recreating the preoperative conditions. The intraoperative decubitus position was then simulated. The resulting geometry was identified using a navigation system and transferred to the simulator, enabling to update the preoperative planning, to compute clinical indices (Cobb angles, etc.) and to simulate the instrumentation maneuvers. Results: The Cobb angle decreased from 34° to 24° between the simulated pre- and intraoperative spine (before the instrumentation). Difference in pedicle screw positioning between the preoperative planning and the intraoperative situation was less than 0.5 mm. The intra-operative simulation of the rod attachment and rotation maneuvers resulted in a 12° Cobb angle. Preliminary conclusion: This study is a first step toward developing an integrated simulator for preoperative planning and intraoperative navigation of scoliotic spine surgeries. Significance: When completed, the new intraoperative simulator will enable to obtain real-time biomechanical feedback during the navigated surgery of a scoliotic spine, and would help to optimize the resulting correction and the instrumentation parameters (instrumented levels, surgical maneuvers, generated forces, etc.). Acknowledgements: Funded by the Canadian Foundation for Innovation / Government of Quebec, and NSERC (Industrial Research Chair with Medtronic).
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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) | 0.000 | 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 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".