Superiority of kinematic alignment over mechanical alignment in total knee arthroplasty during medium‐ to long‐term follow‐up: A meta‐analysis and trial sequential analysis
Bibliographic record
Abstract
PURPOSE: To compare and determine the reliability and conclusiveness of the medium- and long-term efficacy in terms of patient-reported outcome measures and the risk of revisions or reoperations (RRRs) of kinematic alignment (KA) and mechanical alignment (MA) in total knee arthroplasty. METHODS: A comprehensive search was conducted in Medline, EMBASE, Web of Science and Cochrane Database Library to identify relevant literature. Only randomised clinical trials (RCTs) published before July 2023 were included. The Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), Oxford Knee Score (OKS), Forgotten Joint Score (FJS) and RRR were compared. Additionally, OKS and RRR were subjected to a trial sequential analysis. RESULTS: Seven RCTs involving 572 knees were identified. The pooled analysis of the included studies demonstrated that KA showed better medium-term WOMAC and OKS (mean difference [MD] = -6.3, 95% confidence interval [CI]: -9.52 to -2.99, p < 0.05 and MD = 1.1, 95% CI: 0.05-2.15, p < 0.05), respectively), but no significant differences were observed in the long-term follow-up (MD = 2.1, 95% CI: -3.21 to 7.31, not significant [n.s.] and MD = 0.01, 95% CI: -2.43 to 2.46, n.s., respectively). FJS (standardised MD = -0.03, 95% CI: -0.25 to 0.19, n.s.) and RRR (risk ratio = 1.0, 95% CI: 0.57 to 1.74, n.s.) showed no significant intergroup differences (n.s.). The evidence quality ranged from moderate to high, and the trial sequential analysis indicated the need for additional high-quality RCTs to draw more conclusive results. CONCLUSIONS: KA showed better medium-term WOMAC and OKS, while KA and MA had similar FJS without increasing the RRR in medium- and long-term follow-up. Further research is needed for more conclusive results. LEVEL OF EVIDENCE: Level II (meta-analyses).
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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 | 0.032 | 0.053 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.041 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".