PDF(Pubmed)
Abstract：
OBJECTIVE: Pharmacovigilance databases play a critical role in monitoring drug safety. The duplication of reports in pharmacovigilance databases, however, undermines their data integrity. This scoping review sought to provide a comprehensive understanding of duplication in pharmacovigilance databases worldwide.
METHODS: A scoping review.
METHODS: Reviewers comprehensively searched the literature in PubMed, Web of Science, Wiley Online Library, EBSCOhost, Google Scholar and other relevant websites.
METHODS: Peer-reviewed publications and grey literature, without language restriction, describing duplication and/or methods relevant to duplication in pharmacovigilance databases from inception to 1 September 2023.
METHODS: We used the Joanna Briggs Institute guidelines for scoping reviews and conformed with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews. Two reviewers independently screened titles, abstracts and full texts. One reviewer extracted the data and performed descriptive analysis, which the second reviewer assessed. Disagreements were resolved by discussion and consensus or in consultation with a third reviewer.
RESULTS: We screened 22 745 unique titles and 156 were eligible for full-text review. Of the 156 titles, 58 (47 peer-reviewed; 11 grey literature) fulfilled the inclusion criteria for the scoping review. Included titles addressed the extent (5 papers), prevention strategies (15 papers), causes (32 papers), detection methods (25 papers), management strategies (24 papers) and implications (14 papers) of duplication in pharmacovigilance databases. The papers overlapped, discussing more than one field. Advances in artificial intelligence, particularly natural language processing, hold promise in enhancing the efficiency and precision of deduplication of large and complex pharmacovigilance databases.
CONCLUSIONS: Duplication in pharmacovigilance databases compromises risk assessment and decision-making, potentially threatening patient safety. Therefore, efficient duplicate prevention, detection and management are essential for more reliable pharmacovigilance data. To minimise duplication, consistent use of worldwide unique identifiers as the key case identifiers is recommended alongside recent advances in artificial intelligence.
METHODS: A scoping review.
METHODS: Reviewers comprehensively searched the literature in PubMed, Web of Science, Wiley Online Library, EBSCOhost, Google Scholar and other relevant websites.
METHODS: Peer-reviewed publications and grey literature, without language restriction, describing duplication and/or methods relevant to duplication in pharmacovigilance databases from inception to 1 September 2023.
METHODS: We used the Joanna Briggs Institute guidelines for scoping reviews and conformed with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews. Two reviewers independently screened titles, abstracts and full texts. One reviewer extracted the data and performed descriptive analysis, which the second reviewer assessed. Disagreements were resolved by discussion and consensus or in consultation with a third reviewer.
RESULTS: We screened 22 745 unique titles and 156 were eligible for full-text review. Of the 156 titles, 58 (47 peer-reviewed; 11 grey literature) fulfilled the inclusion criteria for the scoping review. Included titles addressed the extent (5 papers), prevention strategies (15 papers), causes (32 papers), detection methods (25 papers), management strategies (24 papers) and implications (14 papers) of duplication in pharmacovigilance databases. The papers overlapped, discussing more than one field. Advances in artificial intelligence, particularly natural language processing, hold promise in enhancing the efficiency and precision of deduplication of large and complex pharmacovigilance databases.
CONCLUSIONS: Duplication in pharmacovigilance databases compromises risk assessment and decision-making, potentially threatening patient safety. Therefore, efficient duplicate prevention, detection and management are essential for more reliable pharmacovigilance data. To minimise duplication, consistent use of worldwide unique identifiers as the key case identifiers is recommended alongside recent advances in artificial intelligence.
摘要：
目的：药物警戒数据库在监测药物安全性方面发挥着关键作用。药物警戒数据库中报告的重复，然而,破坏了他们的数据完整性。本范围审查旨在全面了解全球药物警戒数据库中的重复。
方法：范围审查。
方法：审阅者全面搜索了PubMed中的文献，WebofScience,Wiley在线图书馆,EBSCOhost,GoogleScholar和其他相关网站。
方法：同行评审出版物和灰色文献，没有语言限制，描述药物警戒数据库从开始到2023年9月1日的重复和/或与重复相关的方法。
方法：我们使用JoannaBriggsInstitute指南进行范围审查，并符合系统审查的首选报告项目和范围审查的荟萃分析扩展。两名审稿人独立筛选标题，摘要和全文。一名审查人员提取了数据并进行了描述性分析，第二位审稿人评估了这一点。分歧通过讨论和协商一致或与第三审稿人协商解决。
结果：我们筛选了22745个独特的标题和156个有资格进行全文审查。在156个头衔中，58篇(47篇同行评审；11篇灰色文献)符合范围审查的纳入标准。包括标题处理的程度(5篇论文)，预防策略(15篇论文)，原因(32篇论文)，检测方法(25篇论文)，药物警戒数据库中重复的管理策略(24篇论文)和含义(14篇论文)。论文重叠，讨论不止一个领域。人工智能的进步，特别是自然语言处理，在提高大型和复杂的药物警戒数据库重复数据删除的效率和准确性方面有希望。
结论：药物警戒数据库中的重复会损害风险评估和决策，可能威胁患者安全。因此,有效的重复预防，检测和管理对于更可靠的药物警戒数据至关重要.为了尽量减少重复，与人工智能的最新进展一起，建议一致使用全球唯一标识符作为关键案例标识符。
方法：范围审查。
方法：审阅者全面搜索了PubMed中的文献，WebofScience,Wiley在线图书馆,EBSCOhost,GoogleScholar和其他相关网站。
方法：同行评审出版物和灰色文献，没有语言限制，描述药物警戒数据库从开始到2023年9月1日的重复和/或与重复相关的方法。
方法：我们使用JoannaBriggsInstitute指南进行范围审查，并符合系统审查的首选报告项目和范围审查的荟萃分析扩展。两名审稿人独立筛选标题，摘要和全文。一名审查人员提取了数据并进行了描述性分析，第二位审稿人评估了这一点。分歧通过讨论和协商一致或与第三审稿人协商解决。
结果：我们筛选了22745个独特的标题和156个有资格进行全文审查。在156个头衔中，58篇(47篇同行评审；11篇灰色文献)符合范围审查的纳入标准。包括标题处理的程度(5篇论文)，预防策略(15篇论文)，原因(32篇论文)，检测方法(25篇论文)，药物警戒数据库中重复的管理策略(24篇论文)和含义(14篇论文)。论文重叠，讨论不止一个领域。人工智能的进步，特别是自然语言处理，在提高大型和复杂的药物警戒数据库重复数据删除的效率和准确性方面有希望。
结论：药物警戒数据库中的重复会损害风险评估和决策，可能威胁患者安全。因此,有效的重复预防，检测和管理对于更可靠的药物警戒数据至关重要.为了尽量减少重复，与人工智能的最新进展一起，建议一致使用全球唯一标识符作为关键案例标识符。
