ReviewJournal of clinical medicine2025
Are Current Patient-Reported Outcome Measures Fit for Purpose to Evaluate Unicompartmental Knee Arthroplasty?
Review in Journal of clinical medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- Article
- Are Currently Available Joint-Specific Patient-Reported Outcome Measures Fit for Purpose to Assess the Outcome of Knee Arthroplasty?Journal of clinical medicine · 2025Article
- Enhancing Outcomes in Knee and Hip Arthroplasty: A Multifaceted Approach.Journal of clinical medicine · 2025Article
- Unicompartmental Knee Arthroplasty Offers More Natural Feeling Joints Compared with Total Knee Arthroplasty: A Systematic Review and Meta-Analysis.JB & JS open accessReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The optimal procedure for isolated end-stage medial compartment knee osteoarthritis (OA) remains uncertain, with debate persisting between unicompartmental knee arthroplasty (UKA) and total knee arthroplasty (TKA). The aim of this narrative review is to evaluate current outcome measures in knee arthroplasty (KA) and explore how evolving patient populations and technological advancements may necessitate the use of different patient-reported outcome measures (PROMs) for evaluating UKA. While UKA offers potential advantages over TKA in early pain relief and functional outcomes, most randomised control trials using traditional PROMs have failed to show definitive superiority. The recent introduction of robotic assistance may have further enhanced the benefits of UKA. However, it remains uncertain whether the advantages outweigh the higher revision rates associated with UKA. Although traditional PROMs, such as the Oxford Knee Score or Knee Injury and Osteoarthritis Outcome Score, were designed for the KA population of 30 years ago, they continue to be employed today. The current KA population, particularly those undergoing UKA, are typically younger, physically fitter, and have higher functional demands than those for whom traditional PROMs were originally designed. As a result, these PROMs are now limited by ceiling effects. High-performance PROMs, such as the Forgotten Joint Score-12 or the metabolic equivalent of task score, have recently been utilised for high-demand patients and do not have postoperative ceiling effects. Return to work and sport are also important outcomes that are often overlooked for younger, high-demand patients. Future studies should aim to define the differences between UKA and TKA populations, identify patient factors that predict UKA success, and validate high-performance PROMs for UKA. This will provide deeper insights into the functional benefits of UKA and TKA, enabling patients and surgeons to make more informed decisions regarding implant selection.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.