Comparison of robotic-assisted total knee arthroplasty: an updated systematic review and meta-analysis
Bibliographic record
Abstract
This study was conducted to compare the changes in different clinical scores and imaging indexes of patients who underwent robot-assisted total knee arthroplasty (RA-TKA) and manual total knee arthroplasty (M-TKA). PubMed, Web of Science, Cochrane Library and Embase were searched according to PRISMA guidelines in June 2024. Search terms included "robot-assisted", "manual" and "total knee arthroplasty". Outcome indicators included American Knee Society Score (KSS), Western Ontario McMaster Universities Osteoarthritis Index (WOMAC), Oxford Knee Score (OKS), range of motion (ROM), Hospital for Special Surgery (HSS) score, Forgotten Joint Score (FJS), 36-Item Short Form Health Survey (SF-36), operation duration (min), intraoperative blood loss (ml), pain score, patient's satisfaction scores, hip-knee-ankle (HKA) angle, frontal femoral component angle, frontal tibia component angle, lateral femoral component angle and lateral tibia component angle. A total of 1,033 articles were obtained after removing duplicates, and 12 studies involving 2,863 patients (1,449 RA-TKAs and 1,414 M-TKAs) were finally meta-analyzed (22-32). The baseline data of both groups were similar in all results. Meta-analysis suggested a better performance of the RA-TKA group than the M-TKA group regarding the HKA angle. The manual TKA reduced the operation time and significantly improved the range of motion. The results of > 6 months follow-up showed that M-TKA was better than RA-TKA in terms of KSS score and WOMAC. Compared with M-TKA, RA-TKA can produce more accurate prosthetic alignment, but it does not lead to better clinical results. Orthopedic surgeons should choose between two surgical procedures according to their own experience and patients' characteristics.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.030 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| 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".