No difference in outcomes and gait analysis between mechanical and kinematic knee alignment methods using robotic total knee arthroplasty
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to compare clinical outcomes and perform gait analysis during walking to identify differences in kinematic and kinetic parameters between two alignment methods in robotic-assisted total knee arthroplasty (TKA). METHODS: Sixty patients were randomly assigned to undergo robotic-assisted TKA using either mechanical (30 patients) or kinematic (30 patients) alignment method. Clinical outcomes including varus and valgus laxities, range of motion (ROM), Hospital for Specific Surgery (HSS), Knee Society Score (KSS), and Western Ontario and McMaster Universities (WOMAC) scores and radiological outcomes were evaluated. Gait analysis of 3D spatiotemporal, kinetic, and kinematic parameters during walking was then performed for 10 age and gender matched patients of each group to determine differences between the two alignment methods. RESULTS: The median follow-up duration of the mechanical method group was 8.7 (range 8.1-9.4) years and that of the kinematic method group was 8.4 (range 8.0-9.1) years. Clinical outcomes between the two groups showed no significant difference in HSS, WOMAC, ROM, KS pain, or function score at the last follow-up. No significant difference in varus and valgus laxity assessment, mechanical alignment of the lower limb, or perioperative complications was shown between the two groups. In gait analysis, no significant difference in kinematic or kinetic parameters was found except for varus angle (p < 0.05) and mediolateral ground reaction force (p < 0.05). CONCLUSIONS: Results of this study show that mechanical and kinematic knee alignment methods provide comparable clinical and radiological outcomes after robotic total knee arthroplasty with an average follow-up of 8 years. There were no functional difference during walking between the two alignment methods either. LEVEL OF EVIDENCE: II.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".