Short term outcomes following robotic arm-assisted lateral unicompartmental knee arthroplasty
Bibliographic record
Abstract
Introduction Robotic-arm assisted medial unicompartmental knee arthroplasty (RA-UKA) is associated with improved accuracy of implant positioning and excellent early functional outcomes. However, there is paucity of evidence regarding outcomes following RA-UKA for isolated lateral compartment osteoarthritis. The purpose of this study was to assess the short-term clinical and patient reported outcomes of lateral compartment UKA, utilising robotic-arm assistance. Methods This was a retrospective study of prospectively collected data of 21 consecutive patients who underwent lateral RA-UKA. The study included 9 (42.9%) males and 12 (57.1%) females with a mean age of 63.4 ± 9.2 years. The Oxford Knee Score (OKS) was measured pre-operatively and at 1-year post-operatively, while range of motion (ROM) and complications were also recorded. Results There was significant improvement of OKS at 1 year's follow up compared with the baseline score (21.8 ± 5.6 vs. 45.2 ± 2.8 respectively; p < 0.001). There was also an improvement in pre-operative ROM when compared to ROM at 1 year's follow up (123.5° ± 8° vs. 131.5° ± 6.3° respectively; p < 0.001). None of the study patients underwent revision surgery within 1 year's follow-up. Conclusion In our study, lateral RA-UKA resulted in significant improvements in clinical and patient reported outcomes with low complications rates. Further long-term comparative studies are needed to assess the utility of lateral RA-UKA vs. conventional UKA.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".