Comparison of Kinematic Alignment and Mechanical Alignment in Total Knee Arthroplasty: A Meta‐analysis of Randomized Controlled Clinical Trials
Bibliographic record
Abstract
The aim of this study was to estimate whether kinematic alignment (KA) improves knee function or clinical outcomes compared with mechanical alignment (MA) in the short term after total knee arthroplasty (TKA). We searched the literature for randomized controlled trials published before January 2020 from PubMed, EMBASE, Google, Web of Science, Cochrane Library, and other databases. The observation markers included "The Western Ontario and McMaster Universities (WOMAC) Osteoarthritis Index," "Knee Society Score (KSS)," "Oxford Knee Score (OKS)," "combined Knee Society Score (KSS)," "Knee injury and Osteoarthritis Outcome Score (KOOS)," "European Quality of Life Measure-5 Domain-5-Level (EQ-5D-5L)," range of motion (ROM), lower limb alignment, ligament release, and complications. A total of 11 randomized controlled trial studies were included in the study. During the follow-up of 6-24 months, the KA-TKA group was superior to the MA-TKA group in terms of WOMAC scores, combined KSS, KSS, knee function scores, and knee range of flexion, but there was no significant difference in EQ-5D-5L, KOOS, KOOS (symptoms, pain, ADL, sports, and quality of life), complications, knee range of extension, hip-knee-ankle (HKA) angle, tibial component slope angle, lateral distal femoral angle (LDFA) or medial proximal tibial angle (MPTA) angle between the MA-TKA group and the MA-TKA group (P > 0.05). Our meta-analysis revealed that the incidence of ligament release in the MA-TKA group was higher than that in the KA-TKA group. This meta-analysis shows that the KA-TKA group had better clinical outcomes and knee range of flexion than the MA-TKA group at short-term follow-up.
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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 | 0.027 | 0.052 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.025 | 0.038 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| 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".