Clinical Benefit of Robotic-Assisted Total Knee Arthroplasty over Conventional Total Knee Arthroplasty When Using Mobile-Bearing Implants
Bibliographic record
Abstract
Background and Objectives: As a treatment modality for advanced knee osteoarthritis, total knee arthroplasty is well established and has been performed on many patients over time. To improve surgical outcomes, fixed-bearing implant insertion with robotic-assisted TKA has been introduced; however, the insertion of mobile-bearing (MB) implants with the same method is challenging. The aim of this study was to compare the short-term postoperative follow-up outcomes of MB implant insertion using a robotic-assisted TKA system and conventional TKA. Materials and Methods: We investigated functional improvement in the knees of 60 patients who underwent the insertion of MB implants using either robotic-assisted TKA or conventional TKA. Isokinetic muscular function, range of motion, the Western Ontario and McMaster Universities Osteoarthritis Index score, visual analog scale (VAS) score, and Knee Society Score (KSS) were measured 6 months after surgery. The statistical analysis of outcome measurements was performed using the Mann–Whitney U test and the Wilcoxon signed-rank test. Results: Some isokinetic muscular functions, as well as Knee Society Scores (pain and function) and VAS scores, were significantly higher in patients who underwent MB insertion with robotic-assisted TKA than in those who underwent conventional TKA. Conclusions: When an MB implant is inserted using a robotic-assisted TKA system, a better surgical outcome can be expected.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".