Naming Specific Adverse Effects Improves Relative Recall for Search Filters Identifying Literature on Surgical Interventions in MEDLINE and Embase

Abstract:
A Review of: \nGolder, S., Wright, K., & Loke, Y.K. (2018). The development of search filters for adverse effects of surgical interventions in MEDLINE and Embase.\xa0Health Information and Libraries Journal,\xa035(2), 121-129.\xa0https://doi.org/10.1111/hir.12213 \nAbstract \nObjective – “To develop and validate search filters for MEDLINE and Embase for the adverse effects of surgical interventions” (p.121). \nDesign – From a universe of systematic reviews, the authors created “an unselected cohort…where relevant articles are not chosen because of the presence of adverse effects terms” (p.123). The studies referenced in the cohort reviews were extracted to create an overall citation set. From this, three equal-sized sets of studies were created by random selection, and used for: development of a filter (identifying search terms); evaluation of the filter (testing how well it worked); and validation of the filter (assessing how well it retrieved relevant studies). \nSetting – Systematic reviews of adverse effects from the Database of Abstracts of Reviews of Effects (DARE), published in 2014. \nSubjects – 358 studies derived from the references of 19 systematic reviews (352 available in MEDLINE, 348 available in Embase). \nMethods – Word and phrase frequency analysis was performed on the development set of articles to identify a list of terms, starting with the term creating the highest recall from titles and abstracts of articles, and continuing until adding new search terms produced no more new records recalled. The search strategy thus developed was then tested on the evaluation set of articles. In this case, using the strategy recalled all of the articles which could be obtained using generic search terms; however, adding specific search terms (such as the MeSH term “surgical site infection”) improved recall. Finally, the strategy incorporating both generic and specific search terms for adverse effects was used on the validation set of articles. Search strategies used are included in the article, as is a list in the discussion section of MeSH and Embase indexing terms specific to or suggesting adverse effects. \nMain Results – “In each case the addition of specific adverse effects terms could have improved the recall of the searches” (p. 127). This was true for all six cases (development, evaluation and validation study sets, for each of MEDLINE and Embase) in which specific terms were added to searches using generic terms, and recall percentages compared. \nConclusion – While no filter can deliver 100% of items in a given standard set of studies on adverse effects (since title and abstract fields may not contain any indication of relevance to the topic), adding specific adverse effects terms to generic ones while developing filters is shown to improve recall for surgery-related adverse effects (similarly to drug-related adverse effects). The use of filters requires user engagement and critical analysis; at the same time, deploying well-constructed filters can have many benefits, including: helping users, especially clinicians, get a search started; managing a large and unwieldy set of citations retrieved; and to suggest new search strategies.
Author Listing: Ann Glusker
Volume: 14
Pages: 65-67
DOI: 10.18438/EBLIP29537
Language: English
Journal: Evidence Based Library and Information Practice

Evidence Based Library and Information Practice

EVID BASED LIB INF P

影响因子:0.4 是否综述期刊:否 是否OA:是 是否预警:不在预警名单内 发行时间:- ISSN:1715-720X 发刊频率:- 收录数据库:ESCI/Scopus收录/DOAJ开放期刊 出版国家/地区:Canada 出版社:University of Alberta

期刊介绍

年发文量 19
国人发稿量 -
国人发文占比 0%
自引率 0.0%
平均录取率 -
平均审稿周期 12 Weeks
版面费 -
偏重研究方向 INFORMATION SCIENCE & LIBRARY SCIENCE-
期刊官网 https://journals.library.ualberta.ca/eblip/index.php/EBLIP
投稿链接 -

质量指标占比

研究类文章占比 OA被引用占比 撤稿占比 出版后修正文章占比
100.00% 100.00% - -

相关指数

{{ relationActiveLabel }}
{{ item.label }}

期刊预警不是论文评价,更不是否定预警期刊发表的每项成果。《国际期刊预警名单(试行)》旨在提醒科研人员审慎选择成果发表平台、提示出版机构强化期刊质量管理。

预警期刊的识别采用定性与定量相结合的方法。通过专家咨询确立分析维度及评价指标,而后基于指标客观数据产生具体名单。

具体而言,就是通过综合评判期刊载文量、作者国际化程度、拒稿率、论文处理费(APC)、期刊超越指数、自引率、撤稿信息等,找出那些具备风险特征、具有潜在质量问题的学术期刊。最后,依据各刊数据差异,将预警级别分为高、中、低三档,风险指数依次减弱。

《国际期刊预警名单(试行)》确定原则是客观、审慎、开放。期刊分区表团队期待与科研界、学术出版机构一起,夯实科学精神,打造气正风清的学术诚信环境!真诚欢迎各界就预警名单的分析维度、使用方案、值得关切的期刊等提出建议!

预警情况 查看说明

时间 预警情况
2024年02月发布的2024版 不在预警名单中
2023年01月发布的2023版 不在预警名单中
2021年12月发布的2021版 不在预警名单中
2020年12月发布的2020版 不在预警名单中

JCR分区 WOS分区等级:Q4区

版本 按学科 分区
WOS期刊SCI分区
WOS期刊SCI分区是指SCI官方(Web of Science)为每个学科内的期刊按照IF数值排 序,将期刊按照四等分的方法划分的Q1-Q4等级,Q1代表质量最高,即常说的1区期刊。
(2021-2022年最新版)
INFORMATION SCIENCE & LIBRARY SCIENCE Q4

关于2019年中科院分区升级版(试行)

分区表升级版(试行)旨在解决期刊学科体系划分与学科发展以及融合趋势的不相容问题。由于学科交叉在当代科研活动的趋势愈发显著,学科体系构建容易引发争议。为了打破学科体系给期刊评价带来的桎梏,“升级版方案”首先构建了论文层级的主题体系,然后分别计算每篇论文在所属主题的影响力,最后汇总各期刊每篇论文分值,得到“期刊超越指数”,作为分区依据。

分区表升级版(试行)的优势:一是论文层级的主题体系既能体现学科交叉特点,又可以精准揭示期刊载文的多学科性;二是采用“期刊超越指数”替代影响因子指标,解决了影响因子数学性质缺陷对评价结果的干扰。整体而言,分区表升级版(试行)突破了期刊评价中学科体系构建、评价指标选择等瓶颈问题,能够更为全面地揭示学术期刊的影响力,为科研评价“去四唯”提供解决思路。相关研究成果经过国际同行的认可,已经发表在科学计量学领域国际重要期刊。

《2019年中国科学院文献情报中心期刊分区表升级版(试行)》首次将社会科学引文数据库(SSCI)期刊纳入到分区评估中。升级版分区表(试行)设置了包括自然科学和社会科学在内的18个大类学科。基础版和升级版(试行)将过渡共存三年时间,推测在此期间各大高校和科研院所仍可能会以基础版为考核参考标准。 提示:中科院分区官方微信公众号“fenqubiao”仅提供基础版数据查询,暂无升级版数据,请注意区分。

中科院分区 查看说明

版本 大类学科 小类学科 Top期刊 综述期刊
暂无数据