Accuracy and Outcomes of a Novel Cut-Block Positioning Robotic-Arm Assisted System for Total Knee Arthroplasty: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background The primary objective of this study was to determine the accuracy and precision of component positioning of the ROSA Robotic System for total knee arthroplasty (TKA). Methods A Preferred Reporting Items for Systematic Reviews and Meta-Analysis systematic review was conducted using 4 electronic databases (MEDLINE, EMBASE, Pubmed, and Cochrane Library) to identify all clinical and radiological studies reporting information about the use and results of the ROSA system. The criteria for inclusion were published research articles evaluating the accuracy of component positioning, learning curve, component alignment, complications, and functional outcomes in adults who underwent robotic-assisted TKA. The National Institutes of Health Quality Assessment Tool was used to evaluate the quality of all the included studies. Results A total of 26 studies were assessed for eligibility, and 17 met the inclusion criteria. Nine studies reported on the accuracy and precision of component positioning. The ROSA platform for TKA had a cutting error of less than 0.6 ° for all coronal and sagittal parameters. Pooled analysis demonstrated accuracy within 0.61-1.87 ° and precision within 0.97-1.34 ° when the final intraoperative plan was compared to postoperative radiographs with fewer outliers. Four studies reported improved functional scores with ROSA-assisted TKA than conventional TKA within 1 year of surgery. There was no difference in overall complication rates when compared to conventional TKA. Conclusions The ROSA system is both highly accurate and precise, with fewer outliers when analyzed at various time points, including postoperative standing radiographs. Future studies with robust methodology and longer follow-up are required to demonstrate whether these findings have any clinical benefits in the long term.
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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 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.038 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".