Royle P, Waugh N
Department of Public Health, University of Aberdeen, UK.
Health Technol Assess. 2003;7(34):iii, ix-x, 1-51. doi: 10.3310/hta7340.
To contribute to making searching for Technology Assessment Reports (TARs) more cost-effective by suggesting an optimum literature retrieval strategy.
A sample of 20 recent TARs.
All sources used to search for clinical and cost-effectiveness studies were recorded. In addition, all studies that were included in the clinical and cost-effectiveness sections of the TARs were identified, and their characteristics recorded, including author, journal, year, study design, study size and quality score. Each was also classified by publication type, and then checked to see whether it was indexed in the following databases: MEDLINE, EMBASE, and then either the Cochrane Controlled Trials Register (CCTR) for clinical effectiveness studies or the NHS Economic Evaluation Database (NHS EED) for the cost-effectiveness studies. Any study not found in at least one of these databases was checked to see whether it was indexed in the Science Citation Index (SCI) and BIOSIS, and the American Society of Clinical Oncology (ASCO) Online if a cancer review. Any studies still not found were checked to see whether they were in a number of additional databases.
The median number of sources searched per TAR was 20, and the range was from 13 to 33 sources. Six sources (CCTR, DARE, EMBASE, MEDLINE, NHS EED and sponsor/industry submissions to National Institute for Clinical Excellence) were used in all reviews. After searching the MEDLINE, EMBASE and NHS EED databases, 87.3% of the clinical effectiveness studies and 94.8% of the cost-effectiveness studies were found, rising to 98.2% when SCI, BIOSIS and ASCO Online and 97.9% when SCI and ASCO Online, respectively, were added. The median number of sources searched for the 14 TARs that included an economic model was 9.0 per TAR. A sensitive search filter for identifying non-randomised controlled trials (RCT), constructed for MEDLINE and using the search terms from the bibliographic records in the included studies, retrieved only 85% of the known sample. Therefore, it is recommended that when searching for non-RCT studies a search is done for the intervention alone, and records are then scanned manually for those that look relevant.
Searching additional databases beyond the Cochrane Library (which includes CCTR, NHS EED and the HTA database), MEDLINE, EMBASE and SCI, plus BIOSIS limited to meeting abstracts only, was seldom found to be effective in retrieving additional studies for inclusion in the clinical and cost-effectiveness sections of TARs (apart from reviews of cancer therapies, where a search of the ASCO database is recommended). A more selective approach to database searching would suffice in most cases and would save resources, thereby making the TAR process more efficient. However, searching non-database sources (including submissions from manufacturers, recent meeting abstracts, contact with experts and checking reference lists) does appear to be a productive way of identifying further studies.
通过提出一种优化的文献检索策略，提高查找技术评估报告（TARs）的成本效益。
20份近期TARs的样本。
记录所有用于检索临床和成本效益研究的来源。此外，确定TARs临床和成本效益部分纳入的所有研究，并记录其特征，包括作者、期刊、年份、研究设计、研究规模和质量评分。每项研究还按出版类型分类，然后检查其是否被以下数据库收录：MEDLINE、EMBASE，以及用于临床疗效研究的Cochrane对照试验注册库（CCTR）或用于成本效益研究的英国国家医疗服务体系经济评估数据库（NHS EED）。在这些数据库中至少一个未找到的任何研究，检查其是否被科学引文索引（SCI）、生物学文摘数据库（BIOSIS）收录，以及癌症综述时是否被美国临床肿瘤学会（ASCO）在线数据库收录。仍未找到的任何研究，检查其是否在其他一些数据库中。
每份TAR检索来源的中位数为20个，范围为13至33个来源。所有综述均使用了6个来源（CCTR、DARE、EMBASE、MEDLINE、NHS EED以及向英国国家卫生与临床优化研究所提交的申办方/行业资料）。在检索MEDLINE、EMBASE和NHS EED数据库后，发现了87.3%的临床疗效研究和94.8%的成本效益研究，分别添加SCI、BIOSIS和ASCO在线数据库后升至98.2%，添加SCI和ASCO在线数据库后升至97.9%。包含经济模型的14份TAR检索来源的中位数为每份TAR 9.0个。为MEDLINE构建的用于识别非随机对照试验（RCT）的敏感检索过滤器，使用纳入研究的书目记录中的检索词，仅检索到已知样本的85%。因此，建议在检索非RCT研究时，仅对干预措施进行检索，并手动扫描记录以查找相关记录。
除Cochrane图书馆（包括CCTR、NHS EED和卫生技术评估数据库）、MEDLINE、EMBASE和SCI之外，再加上仅限于会议摘要的BIOSIS，检索其他数据库很少能有效检索到更多研究以纳入TARs的临床和成本效益部分（癌症治疗综述除外，建议检索ASCO数据库）。在大多数情况下，采用更具选择性的数据库检索方法就足够了，并且可以节省资源，从而使TAR流程更高效。然而，检索非数据库来源（包括制造商提交的资料、近期会议摘要、与专家联系以及检查参考文献列表）似乎是识别更多研究的有效方法。
