Cantonal Hospital Zenica, Crkvice 67, 72000, Zenica, Bosnia and Herzegovina.
Sarajevo Medical School, Sarajevo School of Science and Technology, Hrasnička Cesta 3a, 71210, Ilidža, Bosnia and Herzegovina.
Syst Rev. 2023 Mar 28;12(1):56. doi: 10.1186/s13643-023-02223-3.
Systematic reviews (SRs) are invaluable evidence syntheses, widely used in biomedicine and other scientific areas. Tremendous resources are being spent on the production and updating of SRs. There is a continuous need to automatize the process and use the workforce and resources to make it faster and more efficient.
Information gathered by previous EVBRES research was used to construct a questionnaire for round 1 which was partly quantitative, partly qualitative. Fifty five experienced SR authors were invited to participate in a Delphi study (DS) designed to identify the most promising areas and methods to improve the efficient production and updating of SRs. Topic questions focused on which areas of SRs are most time/effort/resource intensive and should be prioritized in further research. Data were analysed using NVivo 12 plus, Microsoft Excel 2013 and SPSS. Thematic analysis findings were used on the topics on which agreement was not reached in round 1 in order to prepare the questionnaire for round 2.
Sixty percent (33/55) of the invited participants completed round 1; 44% (24/55) completed round 2. Participants reported average of 13.3 years of experience in conducting SRs (SD 6.8). More than two thirds of the respondents agreed/strongly agreed the following topics should be prioritized: extracting data, literature searching, screening abstracts, obtaining and screening full texts, updating SRs, finding previous SRs, translating non-English studies, synthesizing data, project management, writing the protocol, constructing the search strategy and critically appraising. Participants have not considered following areas as priority: snowballing, GRADE-ing, writing SR, deduplication, formulating SR question, performing meta-analysis.
Data extraction was prioritized by the majority of participants as an area that needs more research/methods development. Quality of available language translating tools has dramatically increased over the years (Google translate, DeepL). The promising new tool for snowballing emerged (Citation Chaser). Automation cannot substitute human judgement where complex decisions are needed (GRADE-ing).
Study protocol was registered at https://osf.io/bp2hu/ .
系统评价（SR）是非常有价值的证据综合，广泛应用于生物医学和其他科学领域。大量资源用于 SR 的制作和更新。需要不断自动化该过程，并利用劳动力和资源使其更快、更高效。
利用之前 EVBRES 研究收集的信息，为第一轮设计了一份部分定量、部分定性的问卷。邀请 55 名经验丰富的 SR 作者参加一项旨在确定提高 SR 高效制作和更新的最有前途的领域和方法的德尔菲研究（DS）。主题问题集中在哪些领域的 SR 最耗费时间/精力/资源，应优先在进一步研究中进行。使用 NVivo 12 plus、Microsoft Excel 2013 和 SPSS 分析数据。在第一轮未达成一致的主题上使用主题分析结果，以便为第二轮准备问卷。
邀请的 55 名参与者中有 60%（33/55）完成了第一轮；44%（24/55）完成了第二轮。参与者报告的平均 SR 经验为 13.3 年（标准差 6.8）。超过三分之二的受访者同意/强烈同意以下主题应优先考虑：提取数据、文献检索、筛选摘要、获取和筛选全文、更新 SR、查找以前的 SR、翻译非英语研究、综合数据、项目管理、撰写方案、构建搜索策略和批判性评价。参与者没有考虑以下领域作为优先事项：滚雪球法、GRADE 法、撰写 SR、去重、制定 SR 问题、进行荟萃分析。
大多数参与者将数据提取作为需要更多研究/方法开发的领域优先考虑。多年来，可用的语言翻译工具的质量有了显著提高（谷歌翻译、DeepL）。一种新的有前途的滚雪球工具（Citation Chaser）已经出现。自动化不能替代需要复杂决策的人工判断（GRADE 法）。
研究方案在 https://osf.io/bp2hu/ 上注册。
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