Short term outcomes following robotic arm-assisted lateral unicompartmental knee arthroplasty
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it