Publications on diagnostic test evaluation in family medicine journals:

Abstract 

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.

Keywords:  Diagnosis, Sensitivity and specificity, Medline, Family medicine, Logistic regression