Patient-Reported Outcomes of Kinematic vs Mechanical Alignment in Total Knee Arthroplasty: A Systematic Review and Meta-analysis of Randomized Controlled Trials
Bibliographic record
Abstract
Background: Total knee arthroplasty (TKA) is an effective treatment method for severe osteoarthritis of the knee. Poor alignment of a knee replacement has been associated with suboptimal clinical results. Traditionally, mechanical alignment (MA) has been considered the gold standard. In light of reports of decreased satisfaction with TKA, a new technique called kinematic alignment (KA) has been developed. The purpose of this study is to (1) review the results of KA and MA for TKA in randomized controlled trials based on the Western Ontario and McMaster Universities Arthritis Index score, the Oxford Knee Score, and the Knee Society Scores, (2) perform a meta-analyses of the randomized controlled trials with baseline and follow-up values of these parameters, and (3) discuss other shortcomings of this literature from the perspective of study design and execution. Methods: Two independent reviewers performed a systematic review of the English literature using the Embase, Scopus, and PubMed databases searching for randomized controlled trials of MA vs KA in TKA. Of the initial 481 published reports, 6 studies were included in the final review for meta-analysis. The individual studies were then analyzed to evaluate for risks of bias and inconsistencies of methodology. Results: A majority of studies demonstrated low risk of bias. All studies had fundamental technical issues by utilizing different techniques to achieve KA vs MA. There was no significant difference between KA and MA in these studies. Conclusions: There is no significant difference in any outcomes measured between KA and MA in TKA. Both statistical and methodological factors diminish the value of these conclusions.
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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 | 0.037 | 0.092 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.025 | 0.040 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".