Advancing robot-guided techniques in lumbar spine surgery: a systematic review and meta-analysis
Bibliographic record
Abstract
Lumbar spine surgery is a crucial intervention for addressing spinal injuries or conditions affecting the spine, often involving lumbar fusion through pedicle screw (PS) insertion. The precision of PS placement is pivotal in orthopedic surgery. This systematic review compares the accuracy of robot-guided (RG) surgery with free-hand fluoroscopy-guided (FFG), free-hand without fluoroscopy-guided (FHG), and computed tomography image-guided (CTG) techniques for PS insertion. A systematic search of various databases from 1 January 2013 to 30 December 2023 was conducted following PRISMA guidelines. Primary outcomes, including PS insertion accuracy and breach rate, were analyzed using a random-effects model. Risk of bias was assessed using the Newcastle-Ottawa Scale. The overall accuracy of PS insertion using RG, based on 37 studies involving 3,837 patients and 22,117 PS, is 97.9%, with a breach rate of 0.021. RG demonstrated superior accuracy compared to FHG and CTG, with breach rates of 3.4 and 0.015 respectively for RG versus FHG, and 3.8 and 0.026 for RG versus CTG. Additionally, RG was associated with reduced mean estimated blood loss compared to CTG, indicating improved safety. The RG is associated with enhanced accuracy of PS insertion and reduced breach rates over other methods. However, additional randomized controlled trials comparing these modalities are needed for further validation. CRD42023483997
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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 | 0.012 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.022 |
| Bibliometrics | 0.004 | 0.006 |
| 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) | 0.004 | 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".