Unicompartmental Knee Arthroplasty Offers More Natural Feeling Joints Compared with Total Knee Arthroplasty: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Unicompartmental knee arthroplasty (UKA) preserves healthy cartilage and may provide a more "natural-feeling" joint compared with total knee arthroplasty (TKA). The Forgotten Joint Score (FJS) is increasingly used to assess joint awareness. We aimed to systematically compare FJS outcomes in patients undergoing UKA versus TKA. Methods: Following Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines, we searched PubMed, Embase, Scopus, and Web of Science. We included studies reporting FJS in UKA vs. TKA, assessed risk of bias using the Newcastle-Ottawa Scale, and Cochrane RoB-2, and conducted random-effects meta-analyses to calculate pooled mean differences (MD), and sensitivity analyses were performed to assess the robustness of the findings. Results: = 96.24%) and publication bias was detected. Sensitivity analyses, including a leave-one-out analysis and an analysis restricted to randomized controlled trials, confirmed the consistency of the results, with no single study disproportionately influencing the findings. Conclusion: Despite substantial heterogeneity, these findings suggest that UKA may offer superior joint awareness compared with TKA. Level of Evidence: Level III. See Instructions for Authors for a complete description of levels of evidence.
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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 | 0.013 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.025 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".