The Oxford Knee Score: Compared performance before and after knee replacement

Jenny, Jean–Yves; Diesinger, Y.

doi:10.1016/j.otsr.2012.03.004

Cited by 69 publications

(43 citation statements)

References 10 publications

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“…Scores from each item were subsequently added to obtain the global score ranging from 12 to 60 with 12 being the best outcome. In this study, a score of ≤ 18 constitutes a good score while N 18 constitutes a poor score [19].…”

Section: Methodsmentioning

confidence: 82%

Functional Outcome and Quality of Life after Patient-Specific Instrumentation in Total Knee Arthroplasty

Chen

Chin

Tay

et al. 2015

The Journal of Arthroplasty

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Section: Methodsmentioning

confidence: 82%

Functional Outcome and Quality of Life after Patient-Specific Instrumentation in Total Knee Arthroplasty

Chen

Chin

Tay

et al. 2015

The Journal of Arthroplasty

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“…The case described here reveals how one such system, developed to meet local needs, enabled a patient with a knee injury to track his recovery over a two-year period using clinical outcome scores and to reach an OKS of 48/48 and a howRU score of 12/12. Scoring systems even with a low ceiling effect cannot infer complete recovery but can indicate at least a good recovery when viewed in the context of earlier scores (Jenny and Diesinger, 2012) The system here also met the needs of the medical team who were able to track the patient's score away from the clinic in the context of other patients with similar conditions. Aggregated comparison results are available at an individual patient, treatment cohort or specific demographic level.…”

Section: Discussionmentioning

confidence: 99%

An acute knee injury: tracking a two-year recovery online

Taylor

Williams

2015

IJEH

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Current government policy aims to deliver high quality healthcare by recording accurate data at the point of giving care, storing it efficiently and displaying it in a format, which is easily interpreted by healthcare professionals and patients. Few published reports describe the benefits of collecting and reporting such data from the point of view of the patient. We report the case of a 62-year-old lorry driver who used a web-based system to track patient reported outcome measures (PROMs) following an acute knee injury to full recovery over a 24-month period. The system reports clinical outcome scores in real-time to the patient and their medical team, clearly illustrating and enhancing recovery from injury. This case reveals how local software meets the needs of the individual patient linking and integrating such local systems must be the future focus of eHealth within the NHS to release the benefits presented by the information revolution.

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“…Preoperative CT scans for 78 patients pre and post-operative X-rays, body weight in clinical database, Oxford Knee Score 12, 13 (followed by 24-48 months) were collected at Wake Forest Baptist Medical Center (IRB00025566 has been approved prior to the study). All patients underwent UKA surgery with robotic-assisted (MSK, MAKO Surgical Corporation, Fort Lauderdale, FL, USA).…”

Section: Methodsmentioning

confidence: 99%

A systematic approach to predicting the risk of unicompartmental knee arthroplasty revision

Zhang

Smith

Plate

et al. 2016

Osteoarthritis and Cartilage

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Objective Unicompartmental knee arthroplasty (UKA) revision is usually due to the degenerative degree of knee articular osteochondral tissue in the untreated compartment. However, it is difficult to simulate the biomechanical behavior on this tissue accurately. This study presents and validates a reliable system to predict which osteoarthritis patients may suffer revision as a result of biomechanical reasons after having UKA. Design We collected all revision cases available (n=11) and randomly selected 67 UKA cases to keep the revision prevalence of almost 14%. All these 78 cases have been followed at least 2 years. An elastic model is designed to characterize the biomechanical behavior of the articular osteochondral tissue for each patient. After calculated the force on the tissue, finite element method (FEM) is applied to calculating the strain of each tissue node. Kernel Ridge Regression (KRR) method is used to model the relationship between the strain information and the risk of revision. Therefore, the risk of UKA revision can be predicted by this integrated model. Results Leave-one-out cross-validation is implemented to assess the prediction accuracy. As a result, the mean prediction accuracy is 93.58% for all these cases, demonstrating the high value of this model as a decision-making assistant for surgical plaining of knee osteoarthritis. Conclusions The results of this study demonstrated that this integrated model can predict the risk of UKA revision with theoretically high accuracy. It combines bio-mechanical and statistical learning approach to create a surgical planning tool which may support clinical decision in the future.

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The Oxford Knee Score: Compared performance before and after knee replacement

Abstract: Level 2. Exploratory cohort study with universally applied reference standards.

Cited by 69 publications

References 10 publications

Functional Outcome and Quality of Life after Patient-Specific Instrumentation in Total Knee Arthroplasty

Functional Outcome and Quality of Life after Patient-Specific Instrumentation in Total Knee Arthroplasty

An acute knee injury: tracking a two-year recovery online

A systematic approach to predicting the risk of unicompartmental knee arthroplasty revision

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