No Difference in Clinical Outcomes and Survivorship for Robotic, Navigational, and Conventional Primary Total Knee Arthroplasty with a Minimum Follow-up of 10 Years
Bibliographic record
Abstract
Background: Computer-assisted surgery, including robotic and navigational total knee arthroplasty (TKA), has been proposed as a technique used to improve alignment of implants. The purpose of this study was to compare the clinical and radiological outcomes during a minimum follow-up period of 10 years among robotic, navigational, and conventional TKA. Methods: A total of 855 knees (robotic group, 194; conventional group, 270; and navigational group, 391) were available for physical and radiological examinations over a mean follow-up period of 10 years. The survival rate was analyzed using the Kaplan-Meier method based on the survival endpoint. The Hospital for Special Surgery score, Western Ontario and McMaster Universities Osteoarthritis Index, Knee Society Score, and range of motion were used for clinical evaluation. The hip-knee-ankle (HKA) axis angle, the coronal inclination of femoral and tibial components, and the presence of radiolucent lines were also assessed at the final follow-up. Results: = 0.001), but other radiological outcomes were not significantly different among the three groups. Conclusions: Our study demonstrated satisfactory survival rates for robotic, navigational, and conventional TKAs and similar clinical outcomes during the long-term follow-up. Larger studies with continuous serial data are needed to confirm these findings.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".