Dolan P. Although the OKS, and particularly the pain subscale, could be considered an unidimensional measure of highly-correlated items, each item captures a different dimension of pain. Subgroups of older adults with osteoarthritis based upon differing comorbid symptom presentations and potential underlying pain mechanisms. Analyses were performed using Matlab R and Stata. All of these studies used visual inspection of the dendogram clustering diagram in samples of 64 to patients to identify the optimal number of clusters. Conclusion This study developed a cut-off score on a commonly used PROM to provide researchers with a standardised and validated approach to the identification of patients with chronic pain after TKR. Results A high-pain group was identified, described by a maximum of 14 points in the Oxford Knee Score pain subscale six months after surgery.
ERIK SCHIPHORST HAS BEEN CEO OF THE NIJHOF-WASSINK GROUP SINCE 1 APRIL BEFORE THAT, HE WAS .
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beginning, with the TVM teamwe were on the road more often. with the Volvo factory and made pre- calculations. Interactions around money and financial services are a critical part of our lives on and off-line. New technologies and new ways of interacting.
View colleagues of Thecla Schiphorst In particular, we focus on the impact of salespeople and financial professionals on how the research.
Chronic post-surgical pain is widely accepted to be pain of at least three to 6 months duration that develops or increases in intensity after a surgical procedure and significantly affects health-related quality of life [ 1213 ].
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Expectedly, all measures of post-operative pain and health-related quality of life were worse for patients in the chronic-pain group. As such, no further institutional research ethics approvals were required. Patients with chronic pain were characterised by high pain intensity and frequency, with severity, night pain, and limping as the most serious problems.
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Post-operatively, outcomes were also poorer for the high-pain group.
recommender systems, a class of algorithms that calculate which. Eric Paulos. Shannon Cuykendall, Ethan Soutar-Rau, Thecla Schiphorst, Steve DiPaola.
As older adults (OAs) approach retirement, their financial management requirements change as they shift from income to pension or other.
We conducted agglomerative clustering with all patients beginning as individual clusters, which were then successively merged into all possible numbers of clusters, starting at two the natural minimum up to 28 clusters the maximum, where each cluster would correspond to each possible value of the OKS-PS.
Cut-off points for mild, moderate, and severe pain on the visual analogue scale for pain in patients with chronic musculoskeletal pain. Author information Article notes Copyright and License information Disclaimer. ISBN A high-pain group was identified, described by a maximum of 14 points in the Oxford Knee Score pain subscale six months after surgery.
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financial constraints. the Principles and Development of the Calculator: And Other Seminal Writings, eds. 20 Thecla Schiphorst, “A Case Study of Merce Cunningham's Use of the Lifeforms .?doid=; Eric Mullis, “Dance, Here's a Guide,” Financial Times, June 12,
Hierarchical clustering is commonly used, easily interpretable and has been applied in several previous studies on knee pain in order to identify pain profiles, and broader subgroups which are then examined for correlation with pain [ 18 — 21 ].
Agglomerative hierarchical clustering follows a sequence whereby clusters are repetitively merged until a pre-specified number of clusters is achieved, or all participants have been merged into one single cluster. BMJ Open. Health Policy. Finalised data for April to March
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|Our analysis allowed for each item to independently contribute to the generation of pain severity clusters.
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There is no evidence to suggest that multiple cut-off points, for arbitrarily defined patient subgroups, would lead to more efficient classifications, but they would add a level of bias and make their potential application in clinical practice more complex.
Results in blue correspond to hierarchical clustering and pink to k-means clustering.