title: Identifying observational studies of surgical interventions in MEDLINE and EMBASE.
abstract: BACKGROUND
  Health technology assessments of surgical interventions frequently require the inclusion of non-randomised evidence. Literature search strategies employed to identify this evidence often exclude a methodological component because of uncertainty surrounding the use of appropriate search terms. This can result in the retrieval of a large number of irrelevant records. Methodological filters would help to minimise this, making literature searching more efficient.
  
  
  METHODS
  An objective approach was employed to develop MEDLINE and EMBASE filters, using a reference standard derived from screening the results of an electronic literature search that contained only subject-related terms. Candidate terms for MEDLINE (N = 37) and EMBASE (N = 35) were derived from examination of the records of the reference standard. The filters were validated on two sets of studies that had been included in previous health technology assessments.
  
  
  RESULTS
  The final filters were highly sensitive (MEDLINE 99.5%, EMBASE 100%, MEDLINE/EMBASE combined 100%) with precision ranging between 16.7%-21.1%, specificity 35.3%-43.5%, and a reduction in retrievals of over 30%. Against the validation standards, the individual filters retrieved 85.2%-100% of records. In combination, however, the MEDLINE and EMBASE filters retrieved 100% against both validation standards with a reduction in retrieved records of 28.4% and 30.1%
  
  
  CONCLUSIONS
  The MEDLINE and EMBASE filters were highly sensitive and substantially reduced the number of records retrieved, indicating that they are useful tools for efficient literature searching.
authors.affiliation: Health Services Research Unit, University of Aberdeen, Health Sciences Building, Foresterhill, Aberdeen AB25 2ZD, UK. c.fraser@abdn.ac.uk
authors.forename: Cynthia
authors.initials: C
authors.lastname: Fraser
authors.forename: Alison
authors.initials: A
authors.lastname: Murray
authors.forename: Jennifer
authors.initials: J
authors.lastname: Burr
mesh_terms: D016206:Databases, Bibliographic / Q000592:standards / Q000706:statistics & numerical data*; D004526:Efficiency; D019317:Evidence-Based Medicine; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; D016239:MEDLINE / Q000592:standards / Q000706:statistics & numerical data; D011786:Quality Control; D012680:Sensitivity and Specificity; D013514:Surgical Procedures, Operative; D013673:Technology Assessment, Biomedical