在日本患有癌症儿童的症状负担中,报告员间的差异
Tomomi Hayase1, Makiko Naka Mieno2, Naoko Mori3
1Department of Palliative Medicine, Kobe University Hospital, Kobe, Japan.
概括
在日本患有癌症的儿童中,护理人员的报告通常与患者报告的结果 (PRO) 相一致,但存在一些差异. 护理人员的年龄影响了这些差异,影响了症状管理.
科学领域:
- 儿科瘤学 儿科瘤学
- 患者报告的结果 (PROs)
- 症状负担评估 症状负担评估
背景情况:
- 西方研究表明,与自我报告相比,护理人员的代理报告可能会高估儿科癌症患者的症状负担.
- 西方研究结果对亚洲人口,特别是日本的概括性是不确定的.
研究的目的:
- 调查日本癌症儿童和他们的护理人员之间的症状负担在报告员间的差异.
- 探索这些差异与儿童/照顾者特征之间的关联.
主要方法:
- 一项验证研究的二次分析,涉及88个儿童照顾者二 (7-12岁) 和74个二 (13-18岁).
- 对8种 (年轻组) 和31种 (年长组) 症状的症状评分进行了评估.
- 检查了报告员之间的差异和儿童/护理人员特征之间的关系.
主要成果:
- 大多数症状的儿童和护理人员症状得分之间高度一致.
- 在7至12岁儿童的37.5%症状和13至18岁儿童的10.0%症状中,报告者之间发现了显著的差异.
- 护理人员倾向于低估症状负担.
- 护理人员年龄最常与报告员间差异的大小有关.
结论:
- 照顾者代理报告在日本儿科癌症患者的患者报告结果 (PRO) 中似乎可靠,这是由于一般一致性.
- 观察到的差异凸显出需要评估父母-孩子动态,以改善症状管理.
- 进一步研究如何尽量减少报告员之间的差异是有必要的.
更多相关视频
相关概念视频
Factors Affecting Illness
When a person's physical, emotional, intellectual, social development or spiritual functioning is compromised, this deviation from a healthy normal state is called illness. Illness creates stress that in turn harms individuals. Irritation, anger, denial, hopelessness, and fear are behavioral and emotional changes an individual experiences in the phases of illness. A variety of factors influence a person's health and well-being.
For instance, risk factors are connected to illness, disability,...
For instance, risk factors are connected to illness, disability,...
Asthma-III: Symptoms and Complications
Asthma, a common chronic respiratory condition, is classified considering the frequency and severity of symptoms alongside lung function impairment. Understanding this classification is essential for appropriate treatment and management. Here's a detailed look at the classification of asthma and its clinical features and complications:
Classification of Asthma
Classification of Asthma
Bias in Epidemiological Studies
Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:
Cancer Survival Analysis
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...


