ArticleHealth science reports2024
Risk factors for self-reported high symptom cluster burdens in patients with breast cancer undergoing chemotherapy in China: A cross-sectional study.
Article in Health science reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Impact of social support on body image during chemotherapy in patients with breast cancer: The chain mediating role of depression and self-efficacy.Asia-Pacific journal of oncology nursing · 2025Article
- Impact of social support on cognitive function in patients with breast cancer undergoing chemotherapy: The chain-mediating role of fatigue and depression.Asia-Pacific journal of oncology nursing · 2025Article
- Validity and reliability of the Turkish version of the peace, equanimity, and acceptance in the cancer experience (PEACE) scale.Palliative & supportive care · 2025Article
- Network analysis used to investigate the symptoms of cancer patients during chemotherapy: a scoping review.Discover oncology · 2025Review
- Use of machine learning algorithms to construct models of symptom burden cluster risk in breast cancer patients undergoing chemotherapy.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2025Article
- Risk factors for self-reported high symptom cluster burdens in patients with breast cancer undergoing chemotherapy in China: A cross-sectional study.Health science reports · 2024Article
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Authors and funding
9 authors.
Funding
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Abstract
Background and Aims: Further exploration is needed to recognize symptom clusters and categorize subgroups with distinct cluster patterns and associated risks, focusing on symptoms that are highly self-reported by patients with breast cancer undergoing chemotherapy. This study aimed to identify subgroups and risk factors for self-reported high symptom cluster burden among patients with breast cancer undergoing chemotherapy. Methods: A total of 647 participants who met the inclusion criteria were included in the study, with data collected on demographics, disease information, self-reported symptoms, and psychosocial factors. Latent class analysis was utilized to identify the subgroup, while logistic regression was used to pinpoint predictive risk factors. Results: Latent class analysis revealed three subgroups: the "high burden of all symptoms group" ( Conclusion: The study demonstrated the importance of giving more attention to patients with breast cancer who are at risk of developing into membership of high symptom cluster burden group.
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