Developing optimal search strategies for detecting sound clinical prediction studies in MEDLINE.

BACKGROUND: The gaining interest in the use of clinical prediction guides as an aid for helping clinicians make effective front-line decisions, together with the increasing emphasis on evidence-based practice, underscores the need for accurate identification of sound clinical prediction studies. Despite the growing use of clinical prediction guides, little work has been done on identifying optimal literature search filters for retrieving these types of studies. The current study extends our earlier work, on developing optimal search strategies, to include clinical prediction guides.

OBJECTIVE: To develop optimal search strategies for detecting methodologically sound clinical prediction studies in MEDLINE in the publishing year 2000.

DESIGN: Comparison of the retrieval performance of methodologic search strategies in MEDLINE with a manual review ("gold standard") of each article for each issue of 162 core health care journals for the year 2000.

METHODS: 6 experienced research assistants who had been trained and intensively calibrated reviewed all issues of 162 journals for the publishing year 2000. Each article was classified for format, interest, purpose, and methodologic rigor. Search strategies were developed for all purpose categories, including studies of clinical prediction guides.

MAIN OUTCOME MEASURES: The sensitivity (recall), specificity, precision, and accuracy of single and combinations of search terms.

RESULTS: 39% of original studies classified as a clinical prediction guide were methodologically sound. Combinations of terms reached peak sensitivities of 95%. Compared with the best single term, a three-term strategy increased sensitivity for sound studies by 17% (absolute increase), but with some loss of specificity when sensitivity was maximized. When search terms were combined to optimize sensitivity and specificity, these values reached or were close to 90%.

CONCLUSION: Several search strategies can enhance the retrieval of sound clinical prediction studies.