Kinematically Aligned Total Knee Arthroplasty or Mechanically Aligned Total Knee Arthroplasty
Bibliographic record
Abstract
Abstract Kinematically aligned total knee arthroplasty (KATKA) was developed to more anatomically align the knee prosthesis to restore the native alignment of the knee and promote physiological kinematics. Even though there are concerns with implant survival, and follow-up at 10 years or more after KATKA has not been reported, there is a negligible incidence of failure of a tibial component at 2 to 9 years. Early clinical results with this technique are encouraging and demonstrate better functional outcomes compared with mechanically aligned TKA (MATKA). The purpose of this study is to perform a systematic review and meta-analysis of the literature to determine whether there are any clinical differences between KATKA and MATKA. The authors conducted a systematic review of the English literature. Five randomized controlled trials (RCTs) which compared clinical outcomes of KATKA and MATKA were finally included. Four RCTs used patient-specific instrument, and one RCT used navigation. Data were extracted and meta-analysis was conducted. KATKA patients had better outcomes: Mean difference between KATKA and MATKA and p-value are presented in brackets after each variable: the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) (–12.5; p < 0.0001), Oxford Knee Score (OKS) (2.3; p = 0.030), combined Knee Society Score (C-KSS) (13.1; p < 0.0001), Knee Function Score (KFS) (6.4; p = 0.0070), and postoperative range of motion (ROM) (4.1°; p = 0.0010). There was no significant difference concerning the complication rates which needed reoperations or revision surgery (odds ratio, 1.01; p = 0.99). KATKA components had a more femoral valgus (–1.8°; p < 0.0001), more tibial varus (1.2°; p = 0.0001), and more tibial slope (1.2°; p = 0.0001), all being statistically significantly different. Better clinical outcomes were obtained in KATKA and component placement in KATKA is significantly different from that in MATKA. There was no increase of patients with poor clinical results due to implant position especially for varus placement of tibial component. This systematic review of five RCTs suggests that KATKA is of potential alternative method to MATKA since the risk of revision for tibial loosening is negligible compared with MATKA for the same follow-up period.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".