Efficacy and reliability of active robotic-assisted total knee arthroplasty compared with conventional total knee arthroplasty: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: In the field of prosthetics, the ultimate goal is to improve the clinical outcome by using a technique that prolongs the longevity of prosthesis. Active robotic-assisted total knee arthroplasty (TKA) is one such technique that is capable of providing accurate implant position and restoring mechanical alignment. Although relevant studies have been carried out, the differences in the efficacy and reliability between active robotic-assisted TKA and conventional arthroplasty have not yet been adequately discussed. METHODS: We referenced articles, including randomised controlled trials and comparative retrospective research, from PubMed, Embase, Cochrane Library and Web of Science, in order to compare active robotic-assisted TKA with the conventional technique. Data extraction and quality assessment were conducted for each study. Statistical analysis was performed using Revman V. 5.3. RESULTS: Seven studies with a total of 517 knees undergoing TKA were included. Compared with conventional surgery, active robotic TKA showed better outcomes in precise mechanical alignment (mean difference, MD: - 0.82, 95% CI: -1.15 to - 0.49, p < 0.05) and implant position, with lower outliers (p < 0.05), better functional score (Western Ontario and McMaster University, Knee Society Score functional score) and less drainage (MD: - 293.28, 95% CI: - 417.77 to - 168.79, p < 0.05). No significant differences were observed when comparing the operation time, range of motion and complication rates. CONCLUSION: The current research demonstrates that active robotic-assisted TKA surgeries are more capable of improving mechanical alignment and prosthesis implantation when compared with conventional surgery. Further studies are required to investigate the potential benefits and long-term clinical outcomes of active robotic-assisted TKA.
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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 | 0.016 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.022 | 0.036 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".