Evaluating robotic pedicle screw placement against conventional modalities: a systematic review and network meta-analysis
Bibliographic record
Abstract
OBJECTIVE: Several approaches have been studied for internal fixation of the spine using pedicle screws (PSs), including CT navigation, 2D and 3D fluoroscopy, freehand, and robotic assistance. Robot-assisted PS placement has been controversial because training requirements, cost, and previously unclear benefits. This meta-analysis compares screw placement accuracy, operative time, intraoperative blood loss, and overall complications of PS insertion using traditional freehand, navigated, and robot-assisted methods. METHODS: A systematic review was performed of peer-reviewed articles indexed in several databases between January 2000 and August 2021 comparing ≥ 2 PS insertion methods with ≥ 10 screws per treatment arm. Data were extracted for patient outcomes, including PS placement, misplacement, and accuracy; operative time, overall complications, intraoperative blood loss, postoperative hospital length of stay, postoperative Oswestry Disability Index (ODI) score, and postoperative visual analog scale (VAS) score for back pain. Risk of bias was assessed using the Newcastle-Ottawa score and Cochrane tool. A network meta-analysis (NMA) was performed to estimate PS placement accuracy as the primary outcome. RESULTS: Overall, 78 studies consisting of 6262 patients and > 31,909 PSs were included. NMA results showed that robot-assisted and 3D-fluoroscopy PS insertion had the greatest accuracy compared with freehand (p < 0.01 and p < 0.001, respectively), CT navigation (p = 0.02 and p = 0.04, respectively), and 2D fluoroscopy (p < 0.01 and p < 0.01, respectively). The surface under the cumulative ranking (SUCRA) curve method further demonstrated that robot-assisted PS insertion accuracy was superior (S = 0.937). Optimal screw placement was greatest in robot-assisted (S = 0.995) placement, and misplacement was greatest with freehand (S = 0.069) approaches. Robot-assisted placement was favorable for minimizing complications (S = 0.876), while freehand placement had greater odds of complication than robot-assisted (OR 2.49, p < 0.01) and CT-navigation (OR 2.15, p = 0.03) placement. CONCLUSIONS: The results of this NMA suggest that robot-assisted PS insertion has advantages, including improved accuracy, optimal placement, and minimized surgical complications, compared with other PS insertion methods. Limitations included overgeneralization of categories and time-dependent effects.
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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 | 0.019 | 0.042 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.033 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".