Opportunities and challenges for robotic-assisted spine surgery: feasible indications for the MAZOR™ X Stealth Edition
Bibliographic record
Abstract
The clinical use of new technologies has several potential benefits including improved accuracy, precision and efficiency. Robotic assistance during surgery is one such technology and it is making its way into neurosurgical operating rooms with increasing frequency. The Mazor X™Stealth robot was first used in Canada for spine surgery during July 2022 and since then multiple indications for its use have been identified and evaluated.The outcomes of robot-assisted spine surgery have been promising but there is a lack of supportive studies which would serve to refine indications, establish protocols and disseminate practical information. To begin filling this gap we gathered a list of use-cases for which this new technology was successfully employed. In combination with cases that took place in our Centre, we reviewed the existing reported uses of the Mazor X™Stealth for spine surgery and recorded their respective procedures and outcomes for patients and surgeons.Through this review we identified common uses of the Mazor X™Stealth for spine surgery. Usage of robotic-assisted technology had a net positive impact on outcomes for patients as well as surgeons (e.g., improved accuracy of pedicle screw placement and reduced radiation burden). This curation remains a dynamic list, and we foresee the addition of more indications in the future.Clinical Relevance— Enabling the use of technology including robotic systems has the potential to attract clinical research expertise, reduce resource usage and to improve surgical outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".