Devillé W L, Bezemer P D, Bouter L M
Department of Epidemiology and Biostatistics, Medical Faculty, Vrije Universiteit Amsterdam, The Netherlands.
J Clin Epidemiol. 2000 Jan;53(1):65-9. doi: 10.1016/s0895-4356(99)00144-4.
Search strategies for articles reporting on diagnostic test evaluations have been subjected to less research than those in the domain of clinical trials. We set out to develop an optimal search strategy for publications on diagnostic test evaluations in general, that could be added to keywords describing the specific diagnostic test at issue. Nine Family Medicine journals were searched from 1992 through 1995 for primary publications on diagnostic test evaluation by hand searching and a Medline search strategy published earlier. Additionally, new search strategies have been developed with stepwise logistic regression, using Mesh terms and free text words related to diagnosis and test evaluation as independent variables. Hand searching identified 75 primary publications on diagnostic test evaluation from a total of 2467 primary publications. The previously published search strategy had a sensitivity of 73%, a specificity of 94%, and a positive predictive value of 29%. The most accurate new search strategy had a sensitivity of 80.0% (60/75; 95% CI: 71.0-89.1), a specificity of 97.3% (2327/2392; 95% CI; 96.6-97.9%), a positive predictive value of 48% (95% CI: 40-56) and diagnostic odds ratio of 149. All four new strategies used the Mesh term "sensitivity and specificity" (exploded with the Mesh terms "predictive value" and "ROC")and cumulatively added the text words "specificity," "false negative," "accuracy," and "screening." The search strategy using the Mesh term "sensitivity and specificity" (exploded) and the text words "specificity," "false negative," and "accuracy" has both higher sensitivity and specificity than the previously published strategy. The increase in specificity in three strategies reduces the absolute number of false-positive articles that have to be screened by 50-75%, compared to the number of false positives in the earlier strategy.
与临床试验领域相比，针对报道诊断试验评估的文章的检索策略所受到的研究较少。我们着手为一般的诊断试验评估出版物制定一种最佳检索策略，该策略可添加到描述所讨论的特定诊断试验的关键词中。通过手工检索以及使用先前发表的Medline检索策略，对1992年至1995年的九种家庭医学期刊进行了检索，以查找关于诊断试验评估的原始出版物。此外，利用逐步逻辑回归开发了新的检索策略，将与诊断和试验评估相关的医学主题词（Mesh词）和自由文本词作为自变量。手工检索从总共2467篇原始出版物中识别出75篇关于诊断试验评估的原始出版物。先前发表的检索策略灵敏度为73%，特异度为94%，阳性预测值为29%。最准确的新检索策略灵敏度为80.0%（60/75；95%置信区间：71.0 - 89.1），特异度为97.3%（2327/2392；95%置信区间：96.6 - 97.9%），阳性预测值为48%（95%置信区间：40 - 56），诊断比值比为149。所有四种新策略都使用了医学主题词“灵敏度和特异度”（与医学主题词“预测值”和“ROC”一起展开），并累计添加了文本词“特异度”“假阴性”“准确性”和“筛查”。使用医学主题词“灵敏度和特异度”（展开）以及文本词“特异度”“假阴性”和“准确性”的检索策略比先前发表的策略具有更高的灵敏度和特异度。与早期策略中的假阳性数量相比，三种策略中特异度的提高将必须筛查的假阳性文章的绝对数量减少了50 - 75%。
