English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Human Assumed Central Sensitization is involved in the development andmaintenance of chronic low back pain (CLBP). The Central SensitizationInventory (CSI) was developed to evaluate the presence of HACS, with a cut-offvalue of 40/100 based on patients with chronic pain. However, various factorsincluding pain conditions (e.g., CLBP), and gender may influence this cut-offvalue. For chronic pain condition such as CLBP, unsupervised clusteringapproaches can take these factors into consideration and automatically learnthe HACS-related patterns. Therefore, this study aimed to determine the cut-offvalues for a Dutch-speaking population with CLBP, considering the total groupand stratified by gender based on unsupervised machine learning. In this study,questionnaire data covering pain, physical, and psychological aspects werecollected from patients with CLBP and aged-matched pain-free adults (referredto as healthy controls, HC). Four clustering approaches were applied toidentify HACS-related clusters based on the questionnaire data and gender. Theclustering performance was assessed using internal and external indicators.Subsequently, receiver operating characteristic analysis was conducted on thebest clustering results to determine the optimal cut-off values. The studyincluded 151 subjects, consisting of 63 HCs and 88 patients with CLBP.Hierarchical clustering yielded the best results, identifying three clusters:healthy group, CLBP with low HACS level, and CLBP with high HACS level groups.Based on the low HACS levels group (including HC and CLBP with low HACS level)and high HACS level group, the cut-off value for the overall groups were 35, 34for females, and 35 for. The findings suggest that the optimal cut-off valuesfor CLBP is 35. The gender-related cut-off values should be interpreted withcaution due to the unbalanced gender distribution in the sample.

Establishing Central Sensitization Inventory Cut-off Values in patients with Chronic Low Back Pain by Unsupervised Machine Learning