P.208 Open and minimally invasive in-vivo accuracy of pedicle screws with an autonomous robotic system
Bibliographic record
Abstract
Background: Surgical robotics can minimize the discrepancy between surgical preoperative plan and postoperative execution. This work explores the performance of a supervisory-control architecture robot (8i Robotics) for autonomous pedicle instrumentation in both an open and MIS workflow in a poricne model, as well as guidance accuracy in humans. Methods: 11 porcine subjects (7 open, 4 minimally invasive) had clinical grading assessment of pedicle screw placement. 3 of the open cohort had detailed precision analysis. Post-operative CT assessed screw location. Euclidean error was calculated at screw head and screw tip and confidence ellipses generated. In two human patients, guidance accuracy was compared to existing neuro-navigation. Results: All screws where GRS A. There was no clinical difference between clinical assessment of MIS vs Open workflow. Mean tip and head Euclidean error where 2.47+/-1.25mm and 2.25+/-1.25mm respectively. Guidance was successfully obtained in both human cases. Conclusions: 100% of screws obtained satisfactory clinical grading. This demonstrates the capability of a supervisory controlled robotic pedicle screw insertion robot in both open and minimally invasive workflow. Furthermore, initial guidance was feasible in living human patients with comparable agreement to current navigation. This work demonstrates exciting promise for the future of autonomous surgical robotics.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 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".