BACKGROUND AND OBJECTIVES: Physicians frequently search bibliographic databases, such as MEDLINE via PubMed, for best evidence for patient care. The objective of this study was to develop and test search filters to help physicians efficiently retrieve literature related to dialysis (hemodialysis or peritoneal dialysis) from all other articles indexed in PubMed, Ovid MEDLINE, and Embase. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: A diagnostic test assessment framework was used to develop and test robust dialysis filters. The reference standard was a manual review of the full texts of 22,992 articles from 39 journals to determine whether each article contained dialysis information. Next, 1,623,728 unique search filters were developed, and their ability to retrieve relevant articles was evaluated. RESULTS: The high-performance dialysis filters consisted of up to 65 search terms in combination. These terms included the words "dialy" (truncated), "uremic," "catheters," and "renal transplant wait list." These filters reached peak sensitivities of 98.6% and specificities of 98.5%. The filters' performance remained robust in an independent validation subset of articles. CONCLUSIONS: These empirically derived and validated high-performance search filters should enable physicians to effectively retrieve dialysis information from PubMed, Ovid MEDLINE, and Embase.
BACKGROUND AND OBJECTIVES: Physicians frequently search bibliographic databases, such as MEDLINE via PubMed, for best evidence for patient care. The objective of this study was to develop and test search filters to help physicians efficiently retrieve literature related to dialysis (hemodialysis or peritoneal dialysis) from all other articles indexed in PubMed, Ovid MEDLINE, and Embase. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: A diagnostic test assessment framework was used to develop and test robust dialysis filters. The reference standard was a manual review of the full texts of 22,992 articles from 39 journals to determine whether each article contained dialysis information. Next, 1,623,728 unique search filters were developed, and their ability to retrieve relevant articles was evaluated. RESULTS: The high-performance dialysis filters consisted of up to 65 search terms in combination. These terms included the words "dialy" (truncated), "uremic," "catheters," and "renal transplant wait list." These filters reached peak sensitivities of 98.6% and specificities of 98.5%. The filters' performance remained robust in an independent validation subset of articles. CONCLUSIONS: These empirically derived and validated high-performance search filters should enable physicians to effectively retrieve dialysis information from PubMed, Ovid MEDLINE, and Embase.
Authors: A X Garg; A V Iansavichus; M Kastner; L A Walters; N Wilczynski; K A McKibbon; R C Yang; F Rehman; R B Haynes Journal: Kidney Int Date: 2006-10-11 Impact factor: 10.612
Authors: Amit X Garg; Arthur V Iansavichus; Nancy L Wilczynski; Monika Kastner; Leslie A Baier; Salimah Z Shariff; Faisal Rehman; Matthew Weir; K Ann McKibbon; R Brian Haynes Journal: BMJ Date: 2009-09-18
Authors: Katherine E Lynch; Harold I Feldman; Jesse A Berlin; James Flory; Christopher G Rowan; Steven M Brunelli Journal: Am J Kidney Dis Date: 2008-08-15 Impact factor: 8.860
Authors: Ahmed A Al-Jaishi; Kelly Carroll; Cory E Goldstein; Stephanie N Dixon; Amit X Garg; Stuart G Nicholls; Jeremy M Grimshaw; Charles Weijer; Jamie Brehaut; Lehana Thabane; P J Devereaux; Monica Taljaard Journal: Trials Date: 2020-08-28 Impact factor: 2.279