Comparison of robotic or computer-assisted navigation versus fluoroscopic freehand techniques in the accuracy of posterior cervical screw placement during cervical spine surgery: a meta-analysis
Bibliographic record
Abstract
OBJECTIVE: Robot guidance (RG) and computer-assisted navigation (CAN) have been increasingly utilized for posterior cervical screw placement in cervical spine surgery, and cervical screw malposition may contribute to catastrophic complications. However, the superiority of the navigation using RG or CAN compared with conventional freehand (FH) techniques remains controversial, and no meta-analysis comparing the two methods in cervical spine surgery has been performed. METHODS: The PubMed, Embase, Web of Science, Cochrane, China National Knowledge Infrastructure, and Wanfang databases were searched for eligible literature. Studies reporting the primary outcomes of the accuracy of cervical screw placement using RG or CAN compared with FH techniques were included. Bias was evaluated using the Cochrane risk of bias criteria and the Newcastle-Ottawa Scale. The outcomes were evaluated in terms of odds ratio or standardized mean difference and corresponding 95% confidence interval. RESULTS: One randomized controlled trial and 18 comparative cohort studies published between 2012 and 2023 consisting of 946 patients and 4163 cervical screws were included in this meta-analysis. The RG and CAN techniques were associated with a substantially higher rate of optimal and clinically acceptable cervical screw accuracy than FH techniques. Furthermore, compared with the FH group, the navigation group showed fewer postoperative adverse events, less blood loss, shorter hospital lengths of stay, and lower postoperative Neck Disability Index scores. However, the navigation and FH groups had equivalent intraoperative times and postoperative visual analog scale and Japanese Orthopaedic Association scores at the final follow-up. CONCLUSIONS: Both RG and CAN are superior to FH techniques in terms of the accuracy of cervical screw placement. Navigation techniques, including RG and CAN methods, are accurate, safe, and feasible in cervical spine surgery.
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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 | 0.013 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.049 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".