Robot-assisted unicompartmental knee arthroplasty can reduce radiologic outliers compared to conventional techniques
Bibliographic record
Abstract
BACKGROUND: The aim of this study was to compare the clinical and radiologic outcomes of robot-assisted unicompartmental knee arthroplasty (UKA) to those of conventional UKA in Asian patients. METHODS: Fifty-five patients underwent robot-assisted UKA and 57 patients underwent conventional UKA were assessed in this study. Preoperative and postoperative range of motion (ROM), American Knee Society (AKS) score, Western Ontario McMaster University Osteoarthritis Index scale score (WOMAC), and patellofemoral (PF) score values were compared between the two groups. The mechanical femorotibial angle (mFTA) and Kennedy zone were also measured. Coronal alignments of the femoral and tibial components and posterior slopes of the tibial component were compared. Additionally, polyethylene (PE) liner thicknesses were compared. RESULTS: There was no significant difference between the two groups regarding postoperative ROM, AKS, WOMAC and PF score. Robot group showed fewer radiologic outliers in terms of mFTA and coronal alignment of tibial and femoral components (p = 0.022, 0.037, 0.003). The two groups showed significantly different PE liner thicknesses (8.4 ± 0.8 versus 8.8 ± 0.9, p = 0.035). Robot group was the only influencing factor for reducing radiologic outlier (postoperative mFTA) in multivariate model (odds ratio: 2.833, p = 0.037). CONCLUSION: In this study, robot-assisted UKA had many advantages over conventional UKA, such as its ability to achieve precise implant insertion and reduce radiologic outliers. Although the clinical outcomes of robot-assisted UKA over a short-term follow-up period were not significantly different compared to those of conventional UKA, longer follow-up period is needed to determine whether the improved radiologic accuracy of the components in robotic-assisted UKA will lead to better clinical outcomes and improved long-term survival.
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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 | 0.002 |
| 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".