Toward the next generation of simulator for intraoperative navigation of scoliotic spine surgeries
Bibliographic record
Abstract
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 specific screw and rod configuration and instrumentation maneuvers. In this context, the objective of this study is to develop a novel intraoperative simulator for navigated scoliotic spine surgeries. 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, which enabled the updating of the preoperative planning, computing of clinical indices (Cobb angles, etc.) and simulation of instrumentation maneuvers. 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 intraoperative simulation of the rod attachment and rotation maneuvers resulted in a 12° Cobb angle. In conclusion, this preliminary study is a first step toward developing an integrated simulator for preoperative planning and intraoperative navigation of scoliotic spine surgeries. Once completed, the new intraoperative simulator will enable the surgeon to obtain real-time biomechanical feedback during the navigated surgery of a scoliotic spine, and may contribute to improve the resulting correction and instrumentation parameters (instrumented levels, surgical maneuvers, generated forces, etc.).
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".