Robot-assisted Knee Arthroplasty: Randomized Clinical Trial
Bibliographic record
Abstract
BACKGROUND: Osteoarthritis of the knee joint leads to a decrease in the volume of movements, a violation of the sliding of articular surfaces, and a change in the axis of the limb under load, which affects the biomechanics of walking. AIM: This study aims to compare the results of robot-assisted total knee arthroplasty (TKA) and manual techniques, their influence on the biomechanical and podometric parameters of the patient’s walk. METHODS: A prospective randomized study of 68 patients was carried out in the period from 2020 to 2021. Our follow-up period was 1 year. All patients were performed arthroplasty of one knee joint. The main Group “A” included 33 patients TKA with the use of an active robotic setting “TSolution-One” (“THINK Surgical, Inc.” [Fremont, California, USA]); the comparison Group “B” consisted of 35 patients with manual technic of TKA. We studied pain syndrome on the visual analog scale, functional state on the Oxford Knee Score (OKS) and Western Ontario and McMaster Universities Arthritis Index (WOMAC), the volume of ROM movements, and the deviation of the mechanical axis by teleroentgenography of the lower limb. Objective analysis of limb function was performed on the «Alter-G» and the «C-mill». RESULTS: Post-operative pain syndrome on the 1st day after surgery in Group A is stronger by 7.9%, but by the 5th day after surgery in Group A, the pain syndrome is lower by 14.3%. ROM in Group A is better by 16% by 3 months after surgery, after 1 year by 10%. The positioning accuracy of the implant in Group A is 30% better. There are no statistically significant differences in the OKS and WOMAC scales between the groups. The results of restoring normal step in Group A are 13.5% better than in Group B. CONCLUSIONS: Robot-assisted TKA gives more accurate alignment of the mechanical axis, which improves the biomechanics of walking.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".