P.152 In-vivo accuracy of pedicle screws utilizing a supervisory controlled 7DOF robot with OCT guidance
Bibliographic record
Abstract
Background: Pedicle screw fixation is an important technique in spine surgery. Violation of the pedicle can lead to neurovascular injury. Due to excellent pose repeatability, robotic technology may improve accuracy. Existing surgical spine robots use surgical assist architecture. This work explores the performance of a supervisory-control architecture robot (8i Robotics) for autonomous pedicle instrumentation. Methods: 3 porcine subjects underwent pedicle instrumentation utilizing the 7dof robot and were observed for 24 hours. Post-operative CT assessed screw location. Screws were graded clinically with the Gertzbein-Robbins Scale (GRS). Precision was assessed by a customized image processing pipeline. Euclidean error was calculated at screw head and screw tip. All points were normalized to a nominal screw, and confidence ellipses generated. Results: All animals were neurologically intact at 24 hours. All screws where GRS A. Mean tip and head Euclidean error where 2.47+/−1.25mm and 2.25+/-1.25mm respectively. Major and minor axes of the confidence ellipse at 99% was 2.19mm, and 1.28mm, and 2.07mm, and 0.42mm for tip and head respectively. Conclusions: 100% of screws obtained satisfactory clinical grading, with intact function in all animals post-operatively. This shows the capability of a supervisory-controlled 7DOF robot with OCT registration. Further investigation is warranted to further explore robotic capabilities, safety, and cost effectiveness.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".