precision health refers to personalized healthcare based on a person's unique genetic,genomic,or omic composition within the context of lifestyle,social,economic,cultural and environmental influences to help individua...
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precision health refers to personalized healthcare based on a person's unique genetic,genomic,or omic composition within the context of lifestyle,social,economic,cultural and environmental influences to help individuals achieve well-being and optimal *** health utilizes big data sets that combine omics(*** sequence,protein,metabolite,and microbiome information)with clinical information and health outcomes to optimize disease diagnosis,treatment and prevention specific to each *** implementation of precision health requires interprofessional collaboration,community outreach efforts,and coordination of care,a mission that nurses are well-positioned to *** the surge of interest and attention to precision health,most nurses are not well-versed in precision health or its implications for the nursing *** on a critical analysis of literature and expert opinions,this paper provides an overview of precision health and the importance of engaging the nursing profession for its *** topics reviewed in this paper include big data and omics,information science,integration of family health history in precision health,and nursing omics research in symptom *** paper concludes with recommendations for nurse leaders in research,education,clinical practice,nursing administration and policy settings for which to develop strategic plans to implement precision health.
Aims: To examine how symptom cluster subgroups defined by extreme discordant composite scores, cut-off scores, or a median split influence statistical associations with peripheral cytokine levels in women with breast ...
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Aims: To examine how symptom cluster subgroups defined by extreme discordant composite scores, cut-off scores, or a median split influence statistical associations with peripheral cytokine levels in women with breast cancer. Background: Systemic cytokine dysregulation has been posited as a potential biological mechanism underlying symptom clusters in women with breast cancer. Symptom characteristics may play an important role in identifying cytokines of significant etiological importance, however, there is no consensus regarding to the ideal subgrouping technique to use. Design: A secondary analysis of data collected from a cross-sectional descriptive study of women with stage I-II breast cancer was used to examine and compare the relationships between peripheral cytokine levels and symptom subgroups defined by extreme discordant composite scores, cut-off scores, or a median split. Methods: participant symptom scores were transformed into a composite score to account for variability in symptom intensity, frequency and interference. Cytokine levels in subgroups defined by composite scores within the highest and lowest 20% were contrasted with those composed from cut-off scores and a median split. Results: Subgroups defined by the composite score or cut-off scores resulted in similar statistical relationships with cytokine levels in contrast to the median split technique. The use of a median split for evaluating relationships between symptoms clusters and cytokine levels may increase the risk of a type I error. Conclusion: Composite and cut-off scores represent best techniques for defining symptom cluster subgroups in women with breast cancer. Using a consistent approach to define symptom clusters across studies may assist in identifying relevant biological mechanisms.
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