Ovarian Cancer Symptom Clusters: Use of the NIH Symptom Science Model for Precision in Symptom Recognition and Management.
Ovarian Cancer Symptom Clusters: Use of the NIH Symptom Science Model for Precision in Symptom Recognition and Management.
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DOI：
10.1188/22.cjon.533-542
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发表时间：
2022-09-15
影响因子：
1.1
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中科院分区：
文献类型：
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在美国，卵巢癌仍然是最致命的妇科癌症，因为大多数女性被诊断为晚期疾病。虽然早期卵巢肿瘤被认为是无症状的，但女性在整个疾病过程中都会出现症状。本综述识别卵巢癌症状群，并探索美国国立卫生研究院症状科学模型(NIH-SSM)在迅速识别症状和临床干预方面的适用性。使用关键词组合对2000年1月至2022年5月发表的研究进行CINAHL®和PubMed®数据库的重点搜索。NIH-SSM可以指导以精确为重点的干预措施的交付，以解决种族差异并促进以症状为重点的护理的公平。加强对症状生物学的了解可以支持门诊和住院环境中的临床肿瘤科护士。
In the United States, ovarian cancer remains the deadliest gynecologic cancer because most women are diagnosed with advanced disease. Although early-stage ovarian tumors are considered asymptomatic, women experience symptoms throughout disease. This review identifies ovarian cancer symptom clusters and explores the applicability of the National Institutes of Health Symptom Science Model (NIH-SSM) for prompt symptom recognition and clinical intervention. A focused CINAHL® and PubMed® database search was conducted for studies published from January 2000 to May 2022 using combinations of key terms. The NIH-SSM can guide the delivery of precision-focused interventions that address racial disparities and foster equity in symptom-focused care. Enhanced understanding of symptom biology can support clinical oncology nurses in ambulatory and inpatient settings.