Clinical and radiological outcomes of robotic-assisted versus conventional total knee arthroplasty: a systematic review and meta-analysis of randomized controlled trials
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Robotic-assisted total knee arthroplasty (RATKA) is an alternative surgical treatment method to conventional total knee arthroplasty (COTKA) that may deliver better surgical accuracy. However, its impact on patient outcomes is uncertain. The aim of this systematic review of randomized controlled trials (RCTs) is to evaluate whether RATKA could improve functional and radiological outcomes compared with COTKA in adult patients with primary osteoarthritis of the knee. METHODS: We searched Ovid MEDLINE, EMBASE, Scopus, and the Cochrane Library to identify published RCTs comparing RATKA with COTKA. 2 reviewers independently screened eligible studies, reviewed the full texts, assessed risk of bias using the Risk of Bias 2.0 tool, and extracted data. Outcomes were patient-reported outcomes, range of motion, and mechanical alignment (MA) deviation and outliers, and complications. RESULTS: We included 12 RCTs involving 2,200 patients. RATKA probably results in little to no effect on patient-reported outcomes (mean difference (MD) in the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) score of -0.35 (95% confidence interval [CI] -0.78 to 0.07) and range of motion (MD -0.73°; CI -7.5° to 6.0°) compared with COTKA. However, RATKA likely results in a lower degree of MA outliers (risk ratio 0.43; CI 0.27 to 0.67) and less deviation from neutral MA (MD -0.94°; CI -1.1° to -0.73°). There were no differences in revision rate or major adverse effects associated with RATKA. CONCLUSION: Although RATKA likely results in higher radiologic accuracy than COTKA, this may not be clinically meaningful. Also, there is probably no clinically important difference in clinical outcomes between RATKA and COTKA, while it is as yet inconclusive regarding the revision and complication rates due to insufficient evidence.
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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 | 0.018 | 0.055 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.032 |
| Bibliometrics | 0.006 | 0.007 |
| 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".