Current state and future perspectives of spinal navigation and robotics - an AO Spine survey
Bibliographic record
Abstract
Objective: The use of robotics in spine surgery has gained popularity in recent years. This study aims to assess the current state of navigation and robotics in spine surgery and raise awareness of their educational implications across the AO Spine regions. Methods: An online questionnaire comprising 27 questions was distributed to AO spine members between October 25th and November 13th, 2023, using the SurveyMonkey platform (https://www.surveymonkey.com; SurveyMonkey Inc., San Mateo, CA, USA). Statistical analyses (descriptive statistics, Pearson Chi-Square tests) and generation of all graphs were performed using SPSS Version 29.0.1.0 (IBM SPSS Statistic). Results: We received 424 responses from AO Spine members (response rate = 9.9 %). The participants were mostly board-certified orthopedic surgeons (46 %, n=195) and neurosurgeons (32%, n=136) with an equal distribution from academic/non-academic institutions (50 %, n=212). While 49% (n=208) of the participants reported occasional or frequent use of navigation assistance, only 18 % (n=70) indicated the use of robotic assistance for spinal instrumentation. A significant difference based on the country’s median income status (p<0.001) and the respondent’s number of annual instrumentation procedures (p<0.001) has been observed. While 11 % (n=47) of all surgeons use a spinal robot frequently, 36 % (n=153) of the participants stated they don’t need a robot from a current perspective. Most participants (77%, n=301) concluded that high acquisition costs are the primary barrier for the implementation of robotics. Conclusion: Although the hype for robotics in spine surgery increased recently, robotic systems remain non-standard equipment due to cost constraints and limited usability. Spinal navigation appears to have a broader international utilization.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| 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".