Functional Outcomes and Patient Satisfaction in Kinematic vs Mechanical Alignment Total Knee Arthroplasty: A Systematic Review
Bibliographic record
Abstract
Total knee arthroplasty (TKA) is a standard treatment for end-stage osteoarthritis, yet up to 20% of patients remain dissatisfied. Alignment strategy is a critical determinant of outcomes. Mechanical alignment (MA) has long been the conventional approach, while kinematic alignment (KA) has gained attention for its potential to restore native knee anatomy and improve patient-centered results. This systematic review compared functional outcomes, patient satisfaction, and safety between KA and MA in primary TKA. A comprehensive search of PubMed, Web of Science (WOS), Scopus, and the Cochrane Central Register of Controlled Trials (CENTRAL) through August 2025 identified randomized controlled trials and comparative cohort studies reporting functional outcomes, patient-reported measures, satisfaction, or revision rates. Methodological quality was appraised using the Modified Downs and Black checklist. Fourteen studies met the inclusion criteria, representing diverse populations and designs with moderate-to-high methodological quality (scores 22-25/28). KA demonstrated small-to-moderate short- to mid-term improvements in Oxford Knee Score (OKS), Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), Knee Society Score (KSS), Forgotten Joint Score (FJS), and early flexion range of motion, particularly in varus phenotypes and when joint line orientation was preserved. It was also associated with fewer soft-tissue releases and improved intraoperative balance. However, long-term follow-up showed convergence of outcomes, with equivalent survivorship and complication rates between KA and MA. Patient satisfaction trends favored KA during early recovery, though pooled evidence did not demonstrate consistent superiority. KA appears to provide meaningful short- to mid-term advantages in functional recovery, joint awareness, and satisfaction without compromising implant survival or safety, especially in varus-aligned patients when applied within restricted boundaries. Nevertheless, heterogeneity in surgical techniques and outcome reporting highlights the need for large, phenotype-stratified randomized trials with long-term follow-up to establish optimal alignment strategies in TKA.
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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 | 0.007 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".