发展和验证用于退行性颈脊髓病的 MEDLINE 检索过滤器/修饰语。
Development and validation of a MEDLINE search filter/hedge for degenerative cervical myelopathy.
机构信息
Department of Academic Neurosurgery, Cambrige University Hospital, University of Cambridge, Cambridge, UK.
University of Cambridge Medical School, University of Cambridge, Cambridge, UK.
出版信息
BMC Med Res Methodol. 2018 Jul 6;18(1):73. doi: 10.1186/s12874-018-0529-3.
BACKGROUND
Degenerative cervical myelopathy (DCM) is a common condition with many unmet clinical needs. Pooled analysis of studies is an important tool for advancing medical understanding. This process starts with a systematic search of the literature. Identification of studies in DCM is challenged by a number of factors, including non-specific terminology and index terms. Search filters or HEDGEs, are search strings developed and validated to optimise medical literature searches. We aimed to develop a search filter for DCM for the MEDLINE database.
METHODS
The diagnostic test assessment framework of a "development dataset" and seperate "validation dataset" was used. The development dataset was formed by hand searching four leading spinal journals (Spine, Journal of Neurosurgery Spine, Spinal Cord and Journal of Spinal Disorders and Techniques) in 2005 and 2010. The search filter was initially developed focusing on sensitivity and subsequently refined using NOT functions to improve specificity. One validation dataset was formed from DCM narrative and systematic review articles and the second, articles published in April of 1989, 1993, 1997, 2001, 2005, 2009, 2013 and 2017 retrieved via the search MeSH term 'Spine'. Metrics of sensitivity, specificity, precision and accuracy were used to test performance.
RESULTS
Hand searching identified 77/1094 relevant articles for 2005 and 55/1199 for 2010. We developed a search hedge with 100% sensitivity and a precision of 30 and 29% for the 2005 and 2010 development datasets respectively. For the selected time periods, EXP Spine returned 2113 publications and 30 were considered relevant. The search filter identified all 30 relevant articles, with a specificity of 94% and precision of 20%. Of the 255 references listed in the narrative index reviews, 225 were indexed in MEDLINE and 165 (73%) were relevant articles. All relevant articles were identified and accuracy ranged from 67 to 97% over the three reviews. Of the 42 articles returned from 3 recent systematic reviews, all were identified by the filter.
CONCLUSIONS
We have developed a highly sensitive hedge for the research of DCM. Whilst precision is similarly low as other hedges, this search filter can be used as an adjunct for DCM search strategies.
背景
退行性颈脊髓病（DCM）是一种常见病症，存在许多未满足的临床需求。对研究进行汇总分析是推进医学认识的重要工具。这一过程首先要对文献进行系统检索。由于术语和索引词不具有特异性，DCM 的研究识别受到多种因素的挑战。检索过滤器或 Hedge 是为优化医学文献检索而开发和验证的检索字符串。我们旨在为 MEDLINE 数据库开发用于 DCM 的检索过滤器。
方法
使用“开发数据集”和独立的“验证数据集”的诊断测试评估框架。开发数据集由手工检索 2005 年和 2010 年的四本领先的脊柱期刊（Spine、Journal of Neurosurgery Spine、Spinal Cord 和 Journal of Spinal Disorders and Techniques）组成。该搜索过滤器最初是为了提高灵敏度而开发的，然后使用 NOT 函数来提高特异性。一个验证数据集由 DCM 的叙述性和系统评价文章组成，第二个数据集由 1989 年 4 月、1993 年 4 月、1997 年 4 月、2001 年 4 月、2005 年 4 月、2009 年 4 月、2013 年 4 月和 2017 年 4 月通过检索 MeSH 术语“Spine”获得的文章组成。使用灵敏度、特异性、精密度和准确度等指标来测试性能。
结果
手工检索分别在 2005 年和 2010 年确定了 77/1094 篇和 55/1199 篇相关文章。我们为这两个数据集分别开发了一个 100%敏感的 Hedge，其精度分别为 30%和 29%。对于选定的时间段，EXP Spine 返回了 2113 篇出版物，其中 30 篇被认为是相关的。该搜索过滤器识别出了所有 30 篇相关文章，特异性为 94%，精密度为 20%。在叙述性索引综述中列出的 255 篇参考文献中，有 225 篇被 MEDLINE 索引，其中 165 篇（73%）是相关文章。所有相关文章均被识别出来，三篇综述的准确率在 67%至 97%之间。从 4 篇最近的系统综述中返回的 42 篇文章均被过滤器识别。
结论
我们为 DCM 的研究开发了一个高度敏感的 Hedge。虽然其精度与其他 Hedge 相似较低，但这个搜索过滤器可以作为 DCM 搜索策略的辅助手段。
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