Survivorship and Outcomes of Robotic Arm-Assisted Medial Unicompartmental Knee Arthroplasty at a Minimum of 5-Year Follow-Up.
Bibliographic record
Abstract
PURPOSE: This study aimed to evaluate implant survivor-ship, complications, and re-operation rates following robotic arm-assisted unicompartmental knee arthroplasty (UKA) at mid-term follow-up. METHODS: Patient satisfaction, clinical outcome, and knee alignment restoration were evaluated. All patients undergo-ing robotic arm-assisted medial UKA during a 2-year period were prospectively enrolled. Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) score, varus-valgus deformity, and knee range of motion were studied pre- and postoperatively. Revisions and surgery-related complications were recorded. RESULTS: Eighty-five patients were included in the study (mean age: 71.2 years). The mean follow-up was 74.7 months. One conversion to total knee arthroplasty was performed due to periprosthetic fracture 4.5 years after initial surgery result-ing in a survivorship rate of 98.8%. Overall satisfaction was excellent; 97.7% of patients were satisfied or very satisfied, while none was dissatisfied or very dissatisfied. WOMAC score in total, as well as in each component, exhibited sig-nificant improvement postoperatively. Additionally, knee alignment in the coronal plane as well as flexion contracture were significantly improved following the procedure. CONCLUSIONS: The outcomes of the present cohort revealed that precise prosthesis implantation through the robotic arm-assisted system in UKA provided excellent overall satisfac-tion rates and clinical outcomes at mid-term follow-up.
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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 | 0.000 |
| 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) | 0.001 | 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".