在SARS-CoV-2的测序研究中报告的与患者相关的元数据:范围审查和文献识别分析协议
Karen O'Connor1, Davy Weissenbacher2, Amir Elyaderani3
1Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States.
JMIR research protocols
|April 22, 2025
概括
本研究评估了SARS-CoV-2基因组测序研究中的患者元数据. 结果将突出差距,并建议改善更好的传染病研究.
科学领域:
- 基因组流行病学基因组流行病学
- 病毒学 病毒学
- 生物信息学是一种生物信息学.
背景情况:
- 史无前例的SARS-CoV-2测序工作存在,在GISAID和GenBank等公共数据库中有数以百万计的记录.
- 基因组流行病学将遗传数据与患者信息联系起来,以了解传播和疾病的影响.
- 在SARS-CoV-2测序研究中,与患者相关的元数据很少,其质量在很大程度上尚未探索.
研究的目的:
- 量化评估SARS-CoV-2全基因组测序研究中患者报告的元数据的范围和质量.
- 对与SARS-CoV-2基因组数据相关的出版模式进行文献计量分析.
- 评估机器学习分类器在识别相关研究的准确性,以便进行范围审查.
主要方法:
- 利用NIH的LitCovid集合进行序列沉积论文的自动分类.
- 在MEDLINE和PubMed Central中进行独立搜索以验证.
- 使用Covidence提取数据来合成和总结患者元数据的可用性.
- 对作者归属,引用指标和关键词进行文献计量分析.
主要成果:
- 开发并应用于LitCovid出版物的分类模型,直到2023年2月.
- 截至2024年8月的论文处理将于2024年9月开始.
- 文献搜索和选于2024年10月开始;结果将通过表格和图表进行叙述.
结论:
- 范围审查将报告SARS-CoV-2基因组研究中的患者元数据的范围和类型,并确定报告漏洞.
- 将提出建议,以提高患者元数据报告的质量和一致性.
- 图书统计分析将揭示元数据报告的趋势,可能为未来的传染病研究提供信息.
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