Spinal robotics in cervical spine surgery: a systematic review with key concepts and technical considerations
Bibliographic record
Abstract
OBJECTIVE: Spinal robotics for thoracolumbar procedures, predominantly employed for the insertion of pedicle screws, is currently an emerging topic in the literature. The use of robotics in instrumentation of the cervical spine has not been broadly explored. In this review, the authors aimed to coherently synthesize the existing literature of intraoperative robotic use in the cervical spine and explore considerations for future directions and developments in cervical spinal robotics. METHODS: A literature search in the Web of Science, Scopus, and PubMed databases was performed for the purpose of retrieving all articles reporting on cervical spine surgery with the use of robotics. For the purposes of this study, randomized controlled trials, nonrandomized controlled trials, retrospective case series, and individual case reports were included. The Newcastle-Ottawa Scale was utilized to assess risk of bias of the studies included in the review. To present and synthesize results, data were extracted from the included articles and analyzed using the PyMARE library for effect-size meta-analysis. RESULTS: On careful review, 6 articles published between 2016 and 2022 met the inclusion/exclusion criteria, including 1 randomized controlled trial, 1 nonrandomized controlled trial, 2 case series, and 2 case reports. These studies featured a total of 110 patients meeting the inclusion criteria (mean age 53.9 years, range 29-77 years; 64.5% males). A total of 482 cervical screws were placed with the use of a surgical robot, which yielded an average screw deviation of 0.95 mm. Cervical pedicle screws were the primary screw type used, at a rate of 78.6%. According to the Gertzbein-Robbins classification, 97.7% of screws in this review achieved a clinically acceptable grade. The average duration of surgery, blood loss, and postoperative length of stay were all decreased in minimally invasive robotic surgery relative to open procedures. Only 1 (0.9%) postoperative complication was reported, which was a surgical site infection, and the mean length of follow-up was 2.7 months. No mortality was reported. CONCLUSIONS: Robot-assisted cervical screw placement is associated with acceptable rates of clinical grading, operative time, blood loss, and postoperative complications-all of which are equal to or improved relative to the metrics seen in the conventional use of fluoroscopy or computer-assisted navigation for cervical screw placement.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".