Outcome of mobile and fixed unicompartmental knee arthroplasty and risk factors for revision
Bibliographic record
Abstract
Objectives In this study, we aimed to evaluate the outcomes of patients undergoing unilateral knee arthroplasty (UKA) and to analyze risk factors that may lead to revision in patients who undergo UKA. Methods We included patients who underwent mobile or fixed UKA owing to osteoarthritis and who had at least 24 months of follow-up in the postoperative period. We recorded information on patient age, sex, side, body mass (kg/m 2 ), follow-up duration, Knee Society Score, Western Ontario and McMaster Universities Arthritis Index (WOMAC) pain, WOMAC function, WOMAC stiffness, mechanical axle angle, femoral component compliance, tibial component compliance, accumulated experience of the surgeon, and revision status. Results In total, we evaluated 131 knees in 118 patients. 50 (38%) who underwent mobile UKA and 81 (62%) who underwent fixed UKA. The effect of obesity on mobile and fixed UKA revision was significant. The likelihood of revision decreased with greater experience of the surgeon performing UKA. Conclusion Our study showed that the clinical results of mobile and fixed UKA procedures are similar. We also revealed that obesity poses a risk for revision in both fixed and mobile UKA, and the revision rate decreases with increased experience of the surgeon.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".