科学度分析全球贫血研究的分析
Hedye Khaledi1, Fatemeh Makkizadeh1, Afsaneh Hazeri1
1Department of Knowledge and Information Sciences, School of Social Sciences, Yazd University, Yazd, Iran.
International journal of hematology-oncology and stem cell research
|December 20, 2024
概括
这项科学度研究揭示了贫血的关键研究趋势,突出了状细胞和缺铁性贫血. 治疗方面占主导地位,对社会和认知影响的关注有限.
科学领域:
- 血液学 血液学 血液学
- 医疗信息学 医疗信息学
- 图书统计学 图书统计学
背景情况:
- 贫血的特点是红细胞或血红蛋白的低水平.
- 了解贫血研究的智力结构至关重要.
- 本研究提供了2011-2020年贫血研究的全面概述.
研究的目的:
- 绘制贫血研究的知识结构.
- 确定突出的研究集群和新兴主题.
- 分析贫血领域研究趋势的演变.
主要方法:
- 采用了一种描述性-分析性科学测量方法.
- 在PubMed数据库中搜索了与贫血有关的出版物 (2011-2020年).
- 用同词分析,聚类和战略图来进行数据分析.
主要成果:
- 状细胞贫血和缺铁性贫血是最常见的关键词.
- 确定了九个不同的研究集群.
- 新兴的主题包括状细胞贫血的管理,胎儿输血,沙拉西米亚,血清性尿素综合征,慢性功能衰竭中的贫血和干细胞移植.
结论:
- 同词网络揭示了贫血研究中的重要联系.
- 目前的研究主要集中在治疗干预.
- 贫血的社会经济,教育和认知影响需要进一步调查.
更多相关视频
05:08Measurement of Tissue Non-Heme Iron Content using a Bathophenanthroline-Based Colorimetric Assay
Published on: January 31, 2022
4.4K
03:35Determining Gender-Based Differences in Retinal and Choroidal Thickness in Underweight Individuals via Swept-Source Optical Coherence Tomography
Published on: December 1, 2023
276
相关概念视频
01:52Regression Toward the Mean
6.3K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.3K
01:25Statistical Methods for Analyzing Epidemiological Data
299
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
299