Mobile bearing total knee arthroplasty does not lead to better joint awareness compared to fixed bearing design: A systematic review and meta‐analysis
Bibliographic record
Abstract
Abstract Purpose Mobile‐bearing total knee arthroplasty (MB‐TKA) and fixed‐bearing (FB) TKA are both widely used, with MB‐TKA theoretically offering better functional outcomes due to its natural kinematics. This systematic review and meta‐analysis aimed to compare joint awareness between MB‐TKA and FB‐TKA, as measured by Forgotten Joint Score‐12 (FJS‐12), to provide insights into patient‐perceived outcomes. Methods A comprehensive literature search was conducted across major databases following PRISMA guidelines, without date or language restrictions. Studies focusing on TKA with MB or FB as the intervention and control groups, respectively, and reporting on FJS‐12 were included. The selection process involved two independent reviewers. Data extraction was carried out using a structured checklist and assessed for quality using the Newcastle–Ottawa Scale (NOS). The meta‐analysis employed Hedge's g method to compare FJS‐12 and assessed publication bias using Egger's test and funnel plot analyses. Results Six studies, including two randomized clinical trials and four cohort studies with 731 participants and mean follow‐up of 5.4 years, met the inclusion criteria. The meta‐analysis revealed no significant difference in FJS‐12 between MB and FB TKA (pooled difference = 0.132, 95% confidence interval: −0.103 to 0.367, p = 0.271), with moderate heterogeneity observed (I2 = 53.5%). Publication bias assessment indicated no significant bias. Meta‐regression did not identify factors contributing to heterogeneity. Conclusion MB‐TKA does not provide superior patient‐perceived outcomes in terms of joint awareness compared to FB‐TKA. This suggests that the clinical advantage of MB‐TKA in terms of joint awareness is likely negligible. Therefore, the choice between MB and FB TKA should be based on other considerations, such as surgeon preference, implant cost and individual patient needs. Level of Evidence Level III.
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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 | 0.015 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.037 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".