Comparison of Robot-Assisted Versus Fluoroscopy-Guided Transforaminal Lumbar Interbody Fusion(TLIF) for Lumbar Degenerative Diseases: A Systematic Review Meta-Analysis of Trails and Observational Studies
Bibliographic record
Abstract
Abstract Background As an emerging robot-assisted (RA) technology, whether its application in transforaminal lumbar interbody fusion (TLIF) is more worthwhile has not been supported by relevant evidence thus far. Moreover, utilizing RA procedures for TLIF places a greater financial burden on patients when compared to traditional fluoroscopy-guided (FG) TILF. As a result, the appropriateness of implementing RA in TLIF surgery remains uncertain. Objective We aimed to investigate whether the RA TLIF is superior to FG TLIF in treating lumbar degenerative disease. Methods We systematically reviewed PubMed, Embase, Web of Science, CNKI, WanFang, VIP and the Cochrane Library as well as the references of published review articles for relevant studies of comparison of RA versus FG TLIF for lumbar degenerative diseases through July 2023. Cohort studies (CSs) and randomized controlled trials (RCTs) were included. The evaluation criteria consisted of accuracy of percutaneous pedicle screw placement, proximal facet joint violation (FJV), radiation exposure, duration of surgery, estimated blood loss (EBL) and revision case. Quality was assessed using the Cochrane Collaboration tool for RCTs and the Newcastle-Ottawa Scale (NOS) for CSs. Results Our search identified 539 articles, of which 21 met the inclusion criteria for quantitative analysis. Meta-analysis revealed that RA had 1.03-folds higher “clinically acceptable” accuracy than FG (RR: 1.0382, 95% CI: 1.0273–1.0493). And RA had 1.12-folds higher “perfect” accuracy than FG group (RR: 1.1167, 95% CI: 1.0726–1.1626). For proximal FIV, the results suggest that the patients who underwent RA pedicle screw placement had 74% fewer proximal-facet joint violation than the FG group (RR: 0.2606, 95%CI: 0.2063–0.3293). Seventeen CSs and two RCTs reported the duration of time. The results of CSs suggest that there is no significant difference between RA and FG group (SMD: 0.1111, 95%CI: -0.391-0.6131), but the results of RCTs suggest that the patients who underwent RA-TLIF need more surgery time than FG (SMD: 3.7213, 95%CI: 3.0756–4.3669). Sixteen CSs and two RCTs reported the EBL. The results suggest that the patients who underwent RA pedicle screw placement had fewer EBL than FG group (CSs: SMD: -1.9151, 95%CI: -3.1265–0.7036, RCTs: SMD: -5.9010, 95%CI: -8.7238–3.0782). For radiation exposure, the results of CSs suggest that there is no significant difference in radiation time between RA and FG group (SMD: -0.5256, 95%CI: -1.4357-0.3845), but the patients who underwent RA pedicle screw placement had fewer radiation dose than FG group (SMD: -2.2682, 95%CI: -3.1953–1.3411). And four CSs and one RCT reported the number of revision case. The results of CSs suggest that there is no significant difference in the number of revision case between RA and FG group (RR: 0.4087,95% CI 0.1592–1.0495). Our findings are limited by the heterogeneity of the included studies. Conclusion In TLIF, RA technology demonstrates more accurate placement of pedicle screws compared to FG, offering advantages in protecting adjacent facet joints and reducing intraoperative radiation dosage and blood loss. However, due to longer preoperative preparation time, the surgical duration and radiation time of RA is comparable to FG techniques. Currently, FG screw placement continues to be the predominant technique, and clinical surgeons have greater proficiency in its application. Consequently, the integration of RA into TLIF surgery may not be an optimal choice.
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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 | 0.021 | 0.045 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.039 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".