Efficacy and safety of robotic-assisted knee reconstruction: a systematic review and meta-analysis.
Bibliographic record
Abstract
OBJECTIVE: Robotic-assisted surgery is increasingly being utilized in hip and knee reconstruction. However, the relative efficacy and safety of robotic-assisted total knee replacement (RATKR) compared to traditional surgery remained uncertain. This study aimed to systematically review the current literature comparing the outcomes of RATKR to traditional procedures. MATERIALS AND METHODS: Comprehensive literature searches were conducted in major databases to identify studies comparing RATKR with traditional surgeries. The primary outcomes were functional scores and post-operative complications. Pooled mean differences (MDs) with 95% confidence intervals (CIs) were calculated using a random effects model. RESULTS: A total of 12 studies were considered for inclusion. The pooled functional scores of The Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), Knee Society Score (KSS), hospital for Special Surgery (HSS) score, visual analogue score (VAS) pain score showed no significant differences between the two groups (MD = -0.99, 95% CI -2.32 to 0.34, p-value = 0.14). The subgroup analysis for hip and knee reconstructions also revealed no significant difference in terms of functional scores. However, for post-operative complications, while there was no significant difference in terms of blood loss (MD = -1.62, 95% CI -4.42 to 1.17, p-value = 0.25), the readmission rates were significantly higher in the RATKR group (MD = 0.94, 95% CI 0.77 to 1.11, p-value < 0.00001). The overall heterogeneity was extremely high (I² = 93%), particularly in the analyses of post-operative complications. CONCLUSIONS: The findings suggested that robotic-assisted knee reconstruction did not significantly improve functional outcomes compared to traditional surgery. The safety profile was similar except for a higher readmission rate following RATKR. Given the high heterogeneity, further large-scale, well-designed, randomized controlled trials are needed to conclusively determine the efficacy and safety of robotic-assisted hip and knee reconstruction.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.026 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".