2020年以后康复期刊系统审查的报告和方法质量:2020年以后康复期刊系统审查的报告和方法质量:2020年以后康复期刊系统审查的报告和方法质量:2020年以后康复期刊系统审查的报告和方法质量:2020年以后康复期期刊系统审查的报告和方
Takahiro Tsuge1,2,3, Norio Yamamoto3,4, Yosuke Tomita5
1Department of Rehabilitation, Kurashiki Medical Centre, Kurashiki, Okayama 710-8522, Japan.
Physical therapy
|March 16, 2025
概括
2020年系统审查和元分析 (PRISMA) 首选报告项目声明显示,康复期刊中的系统审查 (SR) 仅略有改善. 研究人员必须完全遵守PRISMA 2020指南,以提高报告和方法质量.
科学领域:
- 医学研究方法论医学研究方法论.
- 基于证据的实践实践.
- 科学出版标准的科学出版标准.
背景情况:
- 系统审查 (SRs) 对于综合研究结果至关重要.
- 系统审查和元分析的首选报告项目 (PRISMA) 2020声明旨在提高SR报告的质量.
- 康复期刊对PRISMA 2020的坚持并没有得到充分的证据.
研究的目的:
- 评估PRISMA 2020声明对康复期刊中SRs报告和方法质量的影响.
- 评估遵守PRISMA 2020指南,包括摘要和方法质量评估 (AMSTAR 2).
主要方法:
- 在MEDLINE (PubMed) 的系统搜索中,从2020-2022年在康复期刊上发表的元分析确定了SRs.
- 使用PRISMA 2020的100个SRs与不使用声明的100个SRs进行了比较.
- 评估了对PRISMA 2020项目,PRISMA 2020摘要和AMSTAR 2项目的遵守情况.
主要成果:
- 使用声明 (48.8%) 的SRs对PRISMA 2020项目的坚持略高于不使用声明 (41.5%) 的SRs.
- 在PRISMA 2020声明中,总体报告质量仅略有改善 (9.8%).
- 对于PRISMA 2020的摘要或AMSTAR 2项目,没有观察到显著的改善.
结论:
- 通过PRISMA 2020声明并没有导致康复期刊中SRs报告和方法质量的实质性改善.
- 质量略有改善,强调需要更严格地遵守PRISMA 2020指南.
- 敦促研究人员完全遵守PRISMA 2020的所有项目,以提高系统审查的可靠性和透明度.
相关概念视频
Study Designs in Epidemiology
Epidemiological study designs are fundamental tools for investigating the distribution, determinants, and control of health conditions in populations. They help researchers understand the relationships between exposures and outcomes, and they broadly fall into two categories: "observational" and "experimental" studies.
Observational studies are those where the researcher does not intervene but rather observes natural variations. They include cross-sectional, cohort, and case-control studies.
Observational studies are those where the researcher does not intervene but rather observes natural variations. They include cross-sectional, cohort, and case-control studies.
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:


