Unicompartmental knee arthroplasty: state of the art
Bibliographic record
Abstract
The popularity of unicompartmental knee arthroplasty (UKA) for the treatment of isolated compartment osteoarthritis of the knee has risen over the past 2 decades. Currently, UKA covers 10% of all knee arthroplasties worldwide. Although indications have been extended, results have proven that patient selection plays a critical role in the success of UKA. From the current perspective, age, body mass index, patellofemoral osteoarthritis, anterior cruciate ligament deficiency and chondrocalcinosis are no longer absolute contraindications for UKA. Motivated by the desire to improve survivorship rates, patient-reported outcomes and reduce complications, there have been many technological advances in the field of UKA over the recent years. The aim of this review was to evaluate the current indications, surgical techniques, modes of failure and survivorship results of UKA, by assessing a thorough review of modern literature. Several studies show that innovations in implant design, fixation methods and surgical techniques have led to good-to-excellent long-term survivorship, functional outcomes and less complications. Until now, resurgence of interest of cementless designs is noted according to large national registries to address problems associated with cementation. The future perspective on the usage of UKA, in particular the cementless design, looks promising. Furthermore, there is a growing interest in robotic-assisted techniques in order to optimise result by controlled soft-tissue balancing and reproduce alignment in UKA. Future advances in robotics, most likely in the field of planning and setup, will be valuable in optimising patient-specific UKA.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".