ArticlePain research & management2026
Experimental Pain Phenotypes in Individuals With Symptomatic Knee Osteoarthritis: A Neural Network-Based Clustering Approach.
Article in Pain research & management, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04375072 (Combination Therapy of Home-based Transcranial Direct Current Stimulation and Mindfulness-based Meditation for Self-management of Clinical Pain and Symptoms in Older Adults With Knee Osteoarthritis), which is not on this map. Cited by 1 paper.
What it found
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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.
Combination Therapy of Home-based Transcranial Direct Current Stimulation and Mindfulness-based Meditation for Self-management of Clinical Pain and Symptoms in Older Adults With Knee Osteoarthritis
Who cites it
1 citing paper in PubMed.
- Experimental Pain Phenotypes in Individuals With Symptomatic Knee Osteoarthritis: A Neural Network-Based Clustering Approach.Pain research & management · 2026Article
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Authors and funding
11 authors.
Funding
Abstract
objectiveResearch has emphasized the "phenotyping" of knee osteoarthritis (OA) pain as a priority to effectively target therapies to individual patients. This study identified pain phenotypes based on experimental pain responses in patients with symptomatic knee OA using machine learning approaches and examined their associations with participant characteristics.
methodsIn this cross-sectional study, participants (N = 208; mean age = 67.64 ± 7.51 years) completed demographic, clinical, and psychological questionnaires, followed by a multimodal quantitative sensory testing (QST) battery. For phenotyping, we implemented a two-layer neural network-based k-means algorithm. Participant characteristics were compared across phenotypes using analysis of variance (ANOVA).
resultsFive phenotypes were identified, with marked differences across QST measures: (1) "high pressure pain sensitivity and impaired descending inhibition" (n = 86), characterized by average responses on most QST measures, particularly low pressure pain threshold (PPTh) and impaired conditioned pain modulation (CPM), (2) "high heat pain sensitivity" (n = 40), marked by the lowest heat pain thresholds and tolerances, (3) "low pain sensitivity" (n = 46), exhibiting low sensitivity across most QST measures, (4) "high pain sensitivity and impaired pain modulation" (n = 26), with the lowest PPTh, highest punctate mechanical pain, greatest temporal summation of mechanical pain, lowest CPM, and highest cold pain sensitivity, and (5) "pain-resilient" (n = 10), characterized by low sensitivity across most QST measures, along with the highest PPTh and highest CPM. Phenotypes differed significantly by sex, Kellgren-Lawrence score (index knee), pain severity (Numeric Rating Scale), and OA-related symptoms (Western Ontario and McMaster Universities Osteoarthritis Index), particularly physical function (p < 0.05).
conclusionsThis study identified distinct experimental pain phenotypes, each associated with unique demographic and clinical characteristics. These findings may inform the development of interventions tailored to specific pain profiles to improve pain management and functional outcomes in patients with symptomatic knee OA.
trial registrationClinicalTrials.gov identifier: NCT04375072.
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