Robotic‐assisted medial unicompartmental knee arthroplasty restored prearthritic alignment and led to superior functional outcomes compared with conventional techniques
Bibliographic record
Abstract
PURPOSE: Robotic-assisted medial unicompartmental knee arthroplasty (UKA) can ensure precise preoperative planning, minimise soft tissue damage and restore native coronal alignment. However, few studies have investigated how these advantages translate into differences in early postoperative outcomes. This study aimed to compare differences in early outcomes between conventional UKA (C-UKA) and robotic-assisted UKA (R-UKA). METHODS: This retrospective study investigated two groups of patients who underwent medial UKA: C-UKA group (n = 35) and R-UKA group (n = 35). We assessed (1) serum indicators (hemoglobin, creatine kinase and C-reactive protein) and pain visual analogue scale (VAS) at postoperative days (PODs) 1, 2, 4 and 6; (2) radiologic parameters including joint line height change and arithmetic and mechanical hip-knee-ankle angle (aHKA and mHKA); (3) patient-reported outcomes including Knee Society Scores, Western Ontario and Mcmaster Universities Arthritis Index (WOMAC) and Forgotten Joint Score-12 (FJS-12) at 1-year follow-up. RESULTS: Despite similar serum indicator results, pain VAS was lower in the R-UKA group than in the C-UKA group at PODs 2 (2.5 ± 1.3 vs. 3.6 ± 1.2, p = 0.02), 4 (2.4 ± 0.9 vs. 3.3 ± 1.0, p = 0.03) and 6 (1.9 ± 1.1 vs. 3.1 ± 1.1, p < 0.01). The joint line height change was significantly lower in the R-UKA group than in the C-UKA group (0.9 mm ± 0.6 mm vs. 2.0 mm ± 1.3 mm, p = 0.02). The equivalence test for preoperative aHKA and postoperative mHKA revealed equivalence in only the R-UKA group (p < 0.01). The R-UKA group showed better WOMAC and FJS-12 compared to C-UKA group at 1-year follow-up. CONCLUSION: R-UKA led to lower pain VAS in the early postoperative period compared with C-UKA. Additionally, R-UKA effectively restored the joint line and prearthritic lower limb alignment, resulting in superior functional outcomes at 1-year follow-up compared with C-UKA. 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".