Onlay Tibial Implants Appear to Provide Superior Clinical Results in Robotic Unicompartmental Knee Arthroplasty
Bibliographic record
Abstract
BACKGROUND: Unicompartmental knee arthroplasty (UKA) is an increasingly popular option for the treatment of single-compartment knee osteoarthritis (OA) in adults. Two options for tibial resurfacing during UKA are (1) all-polyethylene inlays and (2) metal-backed onlays. QUESTIONS/PURPOSES: The aim of this study was to determine whether there are any differences in clinical outcomes with inlay versus onlay tibial components. PATIENTS AND METHODS: We identified 39 inlays and 45 onlays, with average 2.7- and 2.3-year follow-up, respectively, from a prospective robotic-assisted surgery database. The primary outcome was the Western Ontario and McMaster University Arthritis Index (WOMAC), subcategorized by the pain, stiffness, and function subscores, at 2 years postoperatively. The secondary outcome was the need for secondary or revision surgery. RESULTS: Postoperative WOMAC pain score was 3.1 for inlays and 1.6 for onlays (p = 0.03). For 25 inlays and 30 onlays with both preoperative and postoperative WOMAC data, pain score improved from 8.3 to 4.0 for inlays versus from 9.2 to 1.7 for onlays (p = 0.01). Function score improved from 27.5 to 12.5 for inlays versus from 32.1 to 7.3 for onlays (p = 0.03). Four inlays and one onlay required a secondary or revision procedure (p = 0.18). CONCLUSIONS: We advise using metal-backed onlays during UKA to improve postoperative clinical outcomes.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".