ArticlePloS one2018
Concordance networks and application to clustering cancer symptomology.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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.
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Who cites it
8 citing papers in PubMed.
- Prevalence and Correlates of Sleep Difficulties in a Large Multicenter Outpatient Oncology Population as Identified through Practice-Based Screening.Cancer research communications · 2026Article
- Network analysis used to investigate the interplay among somatic and psychological symptoms in patients with cancer and cancer survivors: a scoping review.Journal of cancer survivorship : research and practice · 2025Article
- Baseline factors relating to depressive symptoms at one year postoperative in patients with diffuse glioma.Neuro-oncology practice · 2025Article
- PRONA: an R-package for Patient Reported Outcomes Network Analysis.Bioinformatics (Oxford, England) · 2024Article
- Symptom Network Analysis and Unsupervised Clustering of Oncology Patients Identifies Drivers of Symptom Burden and Patient Subgroups With Distinct Symptom Patterns.Cancer medicine · 2024Article
- Symptom clusters among cancer survivors: what can machine learning techniques tell us?BMC medical research methodology · 2021Article
- Clinical data mining on network of symptom and index and correlation of tongue-pulse data in fatigue population.BMC medical informatics and decision making · 2021Article
- Discovery of clinical and demographic determinants of symptom burden in primary brain tumor patients using network analysis and unsupervised clustering.Neuro-oncology advancesArticle
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Symptoms of complex illnesses such as cancer often present with a high degree of heterogeneity between patients. At the same time, there are often core symptoms that act as common drivers for other symptoms, such as fatigue leading to depression and cognitive dysfunction. These symptoms are termed bridge symptoms and when combined with heterogeneity in symptom presentation, are difficult to detect using traditional unsupervised clustering techniques. This article develops a method for identifying patient communities based on bridge symptoms termed concordance network clustering. An empirical study of breast cancer symptomatology is presented, and demonstrates the applicability of this method for identifying bridge symptoms.
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Registered trials
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