Robotic-arm assisted lateral unicompartmental knee arthroplasty: 5-Year outcomes & survivorship
Bibliographic record
Abstract
Introduction Robotic-arm assisted unicompartmental knee arthroplasty (RA-UKA) has demonstrated accurate component positioning and excellent outcomes for medial components. However, there is a paucity of literature on lateral compartment RA-UKA. The purpose of our study was to assess the midterm clinical outcomes and survivorship of lateral RA-UKA. Methods This study was a retrospective review of a single-center prospectively maintained cohort of 33 patients (36 knees) indicated for lateral UKA. Perioperative, and postoperative two- and five-year Knee injury Osteoarthritis Outcome Score (KOOS), Western Ontario and McMaster Universities Osteoarthritis Score (WOMAC), and Forgotten Joint Score (FJS) patient reported outcome measures were collected. Five-year follow-up was recorded in 29 patients (32 knees). Results Mean follow up was 5.1 ± 0.1 years. Mean age and BMI was 70.9 ± 7.2 years and 29.0 ± 4.2 kg/m2, respectively. At discharge, mean distance walked was 273.4 ± 70.4 feet, and mean pain score was 2.0 ± 2.5. At 2-year follow up, mean KOOS, WOMAC, and FJS were 75.1 ± 13.5, 15.0 ± 7.2, and 81.0 ± 23.3, respectively. At 5-year follow up, mean KOOS, WOMAC, and FJS were 75.3 ± 14.6, 14.9 ± 5.0, and 75.8 ± 27.4, respectively. Mean change in KOOS and WOMAC were 35.6 ± 27.1 and 11.7 ± 13.4 ( p< .001 and p< .001). 94% of patients were very satisfied/satisfied, 3% neutral, and 3% dissatisfied. 91% met activity expectations, and 59% were more active than before. Survivorship was 100% at 5 years. Discussion In this study, lateral RA-UKA demonstrated significantly improved clinical outcomes, high patient satisfaction, met expectations, and excellent functional recovery at midterm follow up. Comparative studies are needed to determine differences between robotic-assisted and conventional lateral UKA, as well as TKA.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".