Comparative Study of Clinical and Radiological Outcomes of Conventional Total Knee Replacement (TKR) and Robotic-Assisted TKR in Patients with Bilateral Varus Deformity Knee OA within the Eastern Population
Bibliographic record
Abstract
Background: Osteoarthritis (OA) of the knee, including bilateral varus deformity, presents unique challenges for treatment. This comparative study evaluates clinical and radiological outcomes of conventional total knee replacement (TKR) and robotic-assisted TKR in patients with bilateral varus deformity knee OA within the Eastern population. Methods: A prospective study was conducted on 84 bilateral varus deformity knee OA patients treated with two different surgical approaches: Group A (Conventional TKR) and Group B (Robotic-assisted TKR). Clinical outcomes, including pain relief, functional improvement, and patient satisfaction, were assessed using Visual Analog Scale (VAS), Knee Society Score (KSS), Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), and quality of life measures. Radiological outcomes, including alignment and component positioning, were evaluated. Statistical analyses compared outcomes between groups. Results: Both groups demonstrated significant improvements in clinical and radiological outcomes. Robotic-assisted TKR showed advantages with reduced pain (p < 0.001), superior knee function (p < 0.001), better pain relief and function (p < 0.001), higher patient satisfaction (p < 0.001), and improved quality of life (p < 0.001) compared to conventional TKR. Radiologically, robotic-assisted TKR exhibited superior alignment (p < 0.001) and component positioning (p < 0.001). Implant survivorship remained excellent in both groups, with no revisions reported. Conclusion: Robotic-assisted TKR offers significant benefits in pain relief, functional improvement, and radiological outcomes for patients with bilateral varus deformity knee OA within the Eastern population. While both approaches are effective, the advantages of robotic assistance should be considered in surgical decision-making. Further research is needed to assess cost-effectiveness and long-term outcomes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".