Robot-Assisted versus Conventional Total and Unicompartmental Knee Arthroplasty: A Meta-analysis of Radiological and Functional Outcomes
Bibliographic record
Abstract
The study aims to provide an up-to-date systematic review and meta-analysis comparing radiological and functional outcomes of total knee arthroplasty (TKA) and unicompartmental knee arthroplasty (UKA) using either robotic assistance or conventional methods from the latest assemblage of evidence. This study was conducted according to PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) and MOOSE (Meta-analysis of Observational Studies in Epidemiology) guidelines. All studies in PubMed, EMBASE, Medline, and Cochrane that reported radiological and functional outcomes after TKA or UKA with either robotic or conventional methods were included in the review. Selected endpoints for random effects, pairwise meta-analysis included operative details, radiological outcomes (mechanical axis, component angle deviation, and outliers), and functional outcomes (American Knee Society Score, Knee Society Function Score, revision and complication rate, range of motion (ROM), Hospital for Special Surgery score, and Western Ontario and McMaster Universities Osteoarthritis Index). A total of 23 studies comprising 2,765 knees were included from the initial search. Robot-assisted TKA and UKA were associated with significantly better component angle alignment accuracy (low-to-high quality evidence) at the cost of significantly greater operation time. Robot-assisted UKA was found to have significantly better short-term functional outcomes compared with conventional UKA (moderate-to-high quality evidence). Robot-assisted TKA, however, did not exhibit significantly better short- and midterm subjective knee outcome scores compared with its conventional counterpart (high-quality evidence). Robot-assisted TKA and UKA were associated with nonstatistically significant improved ROM and lesser rates of revision. Robot-assisted total and unicompartmental knee arthroplasty leads to better radiological outcomes, with no significant differences in mid- and long-term functional outcomes compared with conventional methods for the former. Larger prospective studies with mid- and long-term outcomes are required to further substantiate findings from the present study.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.047 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".