Search

A retrospective analysis published by the German Institute for Quality and Efficiency in Health Care (IQWiG) in 2018 concluded that no filter for non-randomized studies (NRS) achieved sufficient sensitivity ([greater than or equal to] 92%), a precondition for comprehensive information retrieval. New NRS filters are therefore required, taking into account the challenges related to this study type. Our evaluation focused on the development of study filters for NRS with a control group ("controlled NRS"), as this study type allows the calculation of an effect size. In addition, we assumed that due to the more explicit search syntax, controlled NRS are easier to identify than non-controlled ones, potentially resulting in better performance measures of study filters for controlled NRS. Our aim was to develop study filters for identifying controlled NRS in PubMed and Ovid MEDLINE. We developed two new search filters that can assist clinicians and researchers in identifying controlled NRS in PubMed and Ovid MEDLINE. The reference set was based on 2110 publications in Medline extracted from 271 Cochrane reviews and on 4333 irrelevant references. The first filter maximizes sensitivity (92.42%; specificity 79.67%, precision 68.49%) and should be used when a comprehensive search is needed. The second filter maximizes specificity (92.06%; precision 82.98%, sensitivity 80.94%) and should be used when a more focused search is sufficient.

Abstract Background Tools to enhance physician searches of Medline and other bibliographic databases have potential to improve the application of new knowledge in patient care. This is particularly true for articles about glomerular disease, which are published across multiple disciplines and are often difficult to track down. Our objective was to develop and test search filters for PubMed, Ovid Medline, and Embase that allow physicians to search within a subset of the database to retrieve articles relevant to glomerular disease. Methods We used a diagnostic test assessment framework with development and validation phases. We read a total of 22,992 full text articles for relevance and assigned them to the development or validation set to define the reference standard. We then used combinations of search terms to develop 997,298 unique glomerular disease filters. Outcome measures for each filter included sensitivity, specificity, precision, and accuracy. We selected optimal sensitive and specific search filters for each database and applied them to the validation set to test performance. Results High performance filters achieved at least 93.8% sensitivity and specificity in the development set. Filters optimized for sensitivity reached at least 96.7% sensitivity and filters optimized for specificity reached at least 98.4% specificity. Performance of these filters was consistent in the validation set and similar among all three databases. Conclusions PubMed, Ovid Medline, and Embase can be filtered for articles relevant to glomerular disease in a reliable manner. These filters can now be used to facilitate physician searching.

Objective: The National Library of Medicine (NLM) inaugurated a "publication type" concept to facilitate searches for systematic reviews (SRs). On the other hand, clinical queries (CQs) are validated search strategies designed to retrieve scientifically sound, clinically relevant original and review articles from biomedical literature databases. We compared the retrieval performance of the SR publication type (SR[pt]) against the most sensitive CQ for systematic review articles (CQrs) in PubMed. Methods: We ran date-limited searches of SR[pt] and CQrs to compare the relative yield of articles and SRs, focusing on the differences in retrieval of SRs by SR[pt] but not CQrs (SR[pt] NOT CQrs) and CQrs NOT SR[pt]. Random samples of articles retrieved in each of these comparisons were examined for SRs until a consistent pattern became evident. Results: For SR[pt] NOT CQrs, the yield was relatively low in quantity but rich in quality, with 79% of the articles being SRs. For CQrs NOT SR[pt], the yield was high in quantity but low in quality, with only 8% being SRs. For CQrs AND SR[pt], the quality was highest, with 92% being SRs. Conclusions: We found that SR[pt] had high precision and specificity for SRs but low recall (sensitivity), whereas CQrs had much higher recall. SR[pt] OR CQrs added valid SRs to the CQrs yield at low cost (i.e., added few non-SRs). For searches that are intended to be exhaustive for SRs, SR[pt] can be added to existing sensitive search filters. [ABSTRACT FROM AUTHOR]

A retrospective analysis published by the German Institute for Quality and Efficiency in Health Care (IQWiG) in 2018 concluded that no filter for non‐randomized studies (NRS) achieved sufficient sensitivity (≥92%), a precondition for comprehensive information retrieval. New NRS filters are therefore required, taking into account the challenges related to this study type. Our evaluation focused on the development of study filters for NRS with a control group ("controlled NRS"), as this study type allows the calculation of an effect size. In addition, we assumed that due to the more explicit search syntax, controlled NRS are easier to identify than non‐controlled ones, potentially resulting in better performance measures of study filters for controlled NRS. Our aim was to develop study filters for identifying controlled NRS in PubMed and Ovid MEDLINE. We developed two new search filters that can assist clinicians and researchers in identifying controlled NRS in PubMed and Ovid MEDLINE. The reference set was based on 2110 publications in Medline extracted from 271 Cochrane reviews and on 4333 irrelevant references. The first filter maximizes sensitivity (92.42%; specificity 79.67%, precision 68.49%) and should be used when a comprehensive search is needed. The second filter maximizes specificity (92.06%; precision 82.98%, sensitivity 80.94%) and should be used when a more focused search is sufficient. [ABSTRACT FROM AUTHOR]

Background: Identifying scientific literature addressing patients' views and preferences is complex due to the wide range of studies that can be informative and the poor indexing of this evidence. Given the lack of guidance we developed a search strategy to retrieve this type of evidence.Methods: We assembled an initial list of terms from several sources, including the revision of the terms and indexing of topic-related studies and, methods research literature, and other relevant projects and systematic reviews. We used the relative recall approach, evaluating the capacity of the designed search strategy for retrieving studies included in relevant systematic reviews for the topic. We implemented in practice the final version of the search strategy for conducting systematic reviews and guidelines, and calculated search's precision and the number of references needed to read (NNR).Results: We assembled an initial version of the search strategy, which had a relative recall of 87.4% (yield of 132/out of 151 studies). We then added some additional terms from the studies not initially identified, and re-tested this improved version against the studies included in a new set of systematic reviews, reaching a relative recall of 85.8% (151/out of 176 studies, 95% CI 79.9 to 90.2). This final version of the strategy includes two sets of terms related with two domains: "Patient Preferences and Decision Making" and "Health State Utilities Values". When we used the search strategy for the development of systematic reviews and clinical guidelines we obtained low precision values (ranging from 2% to 5%), and the NNR from 20 to 50.Conclusions: This search strategy fills an important research gap in this field. It will help systematic reviewers, clinical guideline developers, and policy-makers to retrieve published research on patients' views and preferences. In turn, this will facilitate the inclusion of this critical aspect when formulating heath care decisions, including recommendations. [ABSTRACT FROM AUTHOR]

Objective: The research attempted to develop search filters for biomedical literature databases that improve retrieval of studies of clinical relevance for the nursing and rehabilitation professions. Methods: Diagnostic testing framework compared machine-culled and practitioner-nominated search terms with a hand-tagged clinical literature database. Results: We were unable to: (1) develop filters for nursing, likely because of the overlapping and expanding scope of practice for nurses in comparison with medical professionals, or (2) develop filters for rehabilitation, because of its broad scope and the profession's multifaceted understanding of "health and ability." Conclusions: We found limitations on search filter development for these health professions: nursing and rehabilitation. [ABSTRACT FROM AUTHOR]

Objective: Assess the performance of the SAPHIRE automated information retrieval system.Design: Comparative study of automated and human searching of a MEDLINE test collection.Measurements: Recall and precision of SAPHIRE were compared with those attributes of novice physicians, expert physicians, and librarians for a test collection of 75 queries and 2,334 citations. Failure analysis assessed the efficacy of the Metathesaurus as a concept vocabulary; the reasons for retrieval of nonrelevant articles and nonretrieval of relevant articles; and the effect of changing the weighting formula for relevance ranking of retrieved articles.Results: Recall and precision of SAPHIRE were comparable to those of both physician groups, but less than those of librarians.Conclusion: The current version of the Metathesaurus, as utilized by SAPHIRE, was unable to represent the conceptual content of one-fourth of physician-generated MEDLINE queries. The most likely cause for retrieval of nonrelevant articles was the presence of some or all of the search terms in the article, with frequencies high enough to lead to retrieval. The most likely cause for nonretrieval of relevant articles was the absence of the actual terms from the query, with synonyms or hierarchically related terms present instead. There were significant variations in performance when SAPHIRE's concept-weighing formulas were modified. [ABSTRACT FROM AUTHOR]

Background We frequently fail to identify articles relevant to the subject of acute kidney injury (AKI) when searching the large bibliographic databases such as PubMed, Ovid Medline or Embase. To address this issue, we used computer automation to create information search filters to better identify articles relevant to AKI in these databases. Methods We first manually reviewed a sample of 22 992 full-text articles and used prespecified criteria to determine whether each article contained AKI content or not. In the development phase (two-thirds of the sample), we developed and tested the performance of >1.3-million unique filters. Filters with high sensitivity and high specificity for the identification of AKI articles were then retested in the validation phase (remaining third of the sample). Results We succeeded in developing and validating high-performance AKI search filters for each bibliographic database with sensitivities and specificities in excess of 90%. Filters optimized for sensitivity reached at least 97.2% sensitivity, and filters optimized for specificity reached at least 99.5% specificity. The filters were complex; for example one PubMed filter included >140 terms used in combination, including ‘acute kidney injury’, ‘tubular necrosis’, ‘azotemia’ and ‘ischemic injury’. In proof-of-concept searches, physicians found more articles relevant to topics in AKI with the use of the filters. Conclusions PubMed, Ovid Medline and Embase can be filtered for articles relevant to AKI in a reliable manner. These high-performance information filters are now available online and can be used to better identify AKI content in large bibliographic databases. [ABSTRACT FROM AUTHOR]

Background EMBASE is a popular database used to retrieve biomedical information. Our objective was to develop and test search filters to help clinicians and researchers efficiently retrieve articles with renal information in EMBASE. Study Design We used a diagnostic test assessment framework because filters operate similarly to screening tests. Settings & Participants We divided a sample of 5,302 articles from 39 journals into development and validation sets of articles. Index Test Information retrieval properties were assessed by treating each search filter as a "diagnostic test" or screening procedure for the detection of relevant articles. We tested the performance of 1,936,799 search filters made of unique renal terms and their combinations. Reference Standard & Outcome The reference standard was manual review of each article. We calculated the sensitivity and specificity of each filter to identify articles with renal information. Results The best renal filters consisted of multiple search terms, such as "renal replacement therapy," "renal," "kidney disease," and "proteinuria," and the truncated terms "kidney," "dialy," "neph," "glomerul," and "hemodial." These filters achieved peak sensitivities of 98.7% (95% CI, 97.9-99.6) and specificities of 98.5% (95% CI, 98.0-99.0). The retrieval performance of these filters remained excellent in the validation set of independent articles. Limitations The retrieval performance of any search will vary depending on the quality of all search concepts used, not just renal terms. Conclusions We empirically developed and validated high-performance renal search filters for EMBASE. These filters can be programmed into the search engine or used on their own to improve the efficiency of searching. [ABSTRACT FROM AUTHOR]

Background: Systematic review articles support the advance of science and translation of research evidence into healthcare practice. Inaccurate retrieval frommedline could limit access to reviews. Objective: To determine the quality of indexing systematic reviews and meta-analyses inmedline. Methods: The Clinical Hedges Database, containing the results of a hand search of 161 journals, was used to testmedline indexing terms for their ability to retrieve systematic reviews that met predefined methodologic criteria (labelled as ‘pass’ review articles) and reviews that reported a meta-analysis. Results: The Clinical Hedges Database contained 49 028 articles; 753 were ‘pass’ review articles (552 with a meta-analysis). In total 758 review articles (independent of whether they passed) reported a meta-analysis. The search strategy that retrieved the highest number of ‘pass’ systematic reviews achieved a sensitivity of 97.1%. The publication type ‘meta analysis’ had a false positive rate of 5.6% (95% CI 3.9 to 7.6), and false negative rate of 0.31% (95% CI 0.26 to 0.36) for retrieving systematic reviews that reported a meta-analysis. Conclusions: Inaccuracies in indexing systematic reviews and meta-analyses inmedline can be partly overcome by a 5-term search strategy. Introducing a publication type for systematic reviews of the literature could improve retrieval performance. [ABSTRACT FROM AUTHOR]

Abstract: Objectives: Systematic reviews of the literature are instrumental for bridging research to health care practice and are widely available through databases such as MEDLINE and EMBASE. Search strategies have been developed to aid users in MEDLINE, but no empirical work has been done for EMBASE. The objective of this study was to develop search strategies that optimize the retrieval of methodologically sound systematic reviews from EMBASE. Study Design and Setting: An analytic survey was conducted, comparing hand searches of 55 journals with retrievals from EMBASE for 4,843 candidate search terms and 17,004 combinations. Candidate search strategies were run in EMBASE, the retrievals being compared with the hand search data. The sensitivity, specificity, precision, and accuracy of the search strategies were calculated. Results: Two hundred twenty (16.2%) of the 1,354 articles classified as a review met basic criteria for scientific merit. Combinations of search terms reached peak sensitivities of 94.6% with specificity at 63.7%, whereas combinations of search terms to optimize specificity reached peak specificities of 99.3% with sensitivity at 61.4%. Conclusion: Empirically derived search strategies can achieve high sensitivity and specificity for retrieving methodologically sound systematic reviews from EMBASE. [Copyright &y& Elsevier]

Researchers and practitioners have problems retrieving qualitative studies. Search strategies that can easily and effectively retrieve these studies from large databases such as PsycINFO are therefore important. To determine if search strategies can identify qualitative studies, 64 journals published in 2000 were hand searched using explicit methodological criteria to identify qualitative studies. The authors tested multiple search strategies using 4,985 potential search terms in PsycINFO (Ovid Technologies) and compared the results with the hand search data to calculate retrieval effectiveness. A total of 125 qualitative studies were identified. Single-term and multiple-term strategies had sensitivities (maximizing retrieval of qualitative studies) up to 94.4% and specificities (minimizing retrieval of non qualitative studies and reports) up to 98.6% with ranges of precision and accuracy. Search strategies included terms that were variations of interview, qualitative, themes, and experience. Formal indexing terms performed poorly. Empirically derived search strategies combining textwords can effectively, but not perfectly, retrieve qualitative studies from PsycINFO. [ABSTRACT FROM AUTHOR]

Purpose: To design optimal search strategies for locating sound therapy studies and review articles in CINAHL in the year 2000. Design and Methods: An analytic survey was conducted, comparing hand searches of 75 journals with retrievals from CINAHL for 5,020 candidate search terms and 17,900 combinations for therapy and 5,977 combinations for review articles. All articles were rated with purpose and quality indicators. Candidate search strategies were used in CINAHL, and the retrievals were compared with results of the hand searches. The proposed search strategies were treated as “diagnostic tests” for sound studies and the manual review of the literature was treated as the “gold standard.” Operating characteristics of the search strategies were calculated. Findings: Of the 1,383 articles about treatment, 506 (36.6%) met basic criteria for scientific merit and 127 (17.9%) of the 711 articles classified as a review met the criteria for systematic reviews. For locating sound treatment studies, a three-term strategy maximized sensitivity at 99.4% but with compromised specificity at 58.3%, and a two-term strategy maximized specificity at 98.5% but with compromised sensitivity at 52.0%. For detecting systematic reviews, a three-term strategy maximized sensitivity at 91.3% while keeping specificity high at 95.4%, and a single-term strategy maximized specificity at 99.6% but with compromised sensitivity at 42.5%. Three-term search strategies optimizing sensitivity and specificity achieved these values over 91% for detecting sound treatment studies and over 76% for detecting systematic reviews. Conclusions: Search strategies combining indexing terms and text words can achieve high sensitivity and specificity for retrieving sound treatment studies and review articles in CINAHL. [ABSTRACT FROM AUTHOR]

Objective: The ability to accurately identify articles about therapy in large bibliographic databases such as EMBASE is important for researchers and clinicians. Our study aimed to develop optimal search strategies for detecting sound treatment studies in EMBASE in the year 2000. Methods: Hand searches of journals were compared with retrievals from EMBASE for candidate search strategies. Six trained research assistants reviewed fifty-five journals indexed in EMBASE and rated articles using purpose and quality indicators. Candidate search strategies were developed for identifying treatment articles and then tested, and the retrievals were compared with the hand-search data. The operating characteristics of the strategies were calculated. Results: Three thousand eight hundred fifty articles were original studies on treatment, of which 1,256 (32.6%) were methodologically sound. Combining search terms revealed a top performing strategy (random:.tw. OR clinical trial:.mp. OR exp health care quality) with sensitivity of 98.9% and specificity of 72.0%. Maximizing specificity, a top performing strategy (double-blind:.mp. OR placebo:.tw. OR blind: .tw.) achieved a value over 96.0%, but with compromised sensitivity at 51.7%. A 3-term strategy achieved the best optimization of sensitivity and specificity (random:.tw. OR placebo:.mp. OR double-blind:.tw.), with both these values over 92.0%. Conclusion: Search strategies can achieve high performance for retrieving sound treatment studies in EMBASE. [ABSTRACT FROM AUTHOR]

Background: Clinical end users of MEDLINE have a difficult time retrieving articles that are both scientifically sound and directly relevant to clinical practice. Search filters have been developed to assist end users in increasing the success of their searches. Many filters have been developed for the literature on therapy and reviews but little has been done in the area of prognosis. The objective of this study is to determine how well various methodologic textwords, Medical Subject Headings, and their Boolean combinations retrieve methodologically sound literature on the prognosis of health disorders in MEDLINE. Methods: An analytic survey was conducted, comparing hand searches of journals with retrievals from MEDLINE for candidate search terms and combinations. Six research assistants read all issues of 161 journals for the publishing year 2000. All articles were rated using purpose and quality indicators and categorized into clinically relevant original studies, review articles, general papers, or case reports. The original and review articles were then categorized as 'pass' or 'fail' for methodologic rigor in the areas of prognosis and other clinical topics. Candidate search strategies were developed for prognosis and run in MEDLINE -- the retrievals being compared with the hand search data. The sensitivity, specificity, precision, and accuracy of the search strategies were calculated. Results: 12% of studies classified as prognosis met basic criteria for scientific merit for testing clinical applications. Combinations of terms reached peak sensitivities of 90%. Compared with the best single term, multiple terms increased sensitivity for sound studies by 25.2% (absolute increase), and increased specificity, but by a much smaller amount (1.1%) when sensitivity was maximized. Combining terms to optimize both sensitivity and specificity achieved sensitivities and specificities of approximately 83% for each. Conclusion: Empirically derived search strategies combining indexing terms and textwords can achieve high sensitivity and specificity for retrieving sound prognostic studies from MEDLINE. [ABSTRACT FROM AUTHOR]

Provides tips on how to store and retrieve medical literature. Factors to consider in designing a personal filing system; Physical storage of personal file; Subject headings to be used.

Background: A major barrier to the practice of evidence-based medicine is efficiently finding scientifically sound studies on a given clinical topic.Objective: To investigate a deep learning approach to retrieve scientifically sound treatment studies from the biomedical literature.Methods: We trained a Convolutional Neural Network using a noisy dataset of 403,216 PubMed citations with title and abstract as features. The deep learning model was compared with state-of-the-art search filters, such as PubMed's Clinical Query Broad treatment filter, McMaster's textword search strategy (no Medical Subject Heading, MeSH, terms), and Clinical Query Balanced treatment filter. A previously annotated dataset (Clinical Hedges) was used as the gold standard.Results: The deep learning model obtained significantly lower recall than the Clinical Queries Broad treatment filter (96.9% vs 98.4%; P<.001); and equivalent recall to McMaster's textword search (96.9% vs 97.1%; P=.57) and Clinical Queries Balanced filter (96.9% vs 97.0%; P=.63). Deep learning obtained significantly higher precision than the Clinical Queries Broad filter (34.6% vs 22.4%; P<.001) and McMaster's textword search (34.6% vs 11.8%; P<.001), but was significantly lower than the Clinical Queries Balanced filter (34.6% vs 40.9%; P<.001).Conclusions: Deep learning performed well compared to state-of-the-art search filters, especially when citations were not indexed. Unlike previous machine learning approaches, the proposed deep learning model does not require feature engineering, or time-sensitive or proprietary features, such as MeSH terms and bibliometrics. Deep learning is a promising approach to identifying reports of scientifically rigorous clinical research. Further work is needed to optimize the deep learning model and to assess generalizability to other areas, such as diagnosis, etiology, and prognosis. [ABSTRACT FROM AUTHOR]

The article offers the author's views on an attractive array of information services that can help people take evidence-based health care decisions. The 5S model for the organization of evidence-based health care information services provide a full range of evidence concerning management options for a given health problem. The model is being extensively used by physicians as well as patients.

A correction to the article "Development and Validation of Filters for the Retrieval of Studies of Clinical Examination From Medline," that was published in the 2011 issue is presented.

Visits to the web site www.acpjc.org, the Web version of the "ACP Journal Club," are picking up as subscribers come to realize its many advantages. For the past decade, acpjc.org has provided the highest-quality, most relevant, and highest interest-rated articles for internal medicine and its subspecialties. The Web site is being continuously upgraded. "ACP Journal Club," classifies articles according to their objective, using the categories therapeutics, diagnosis, prognosis, clinical prediction, etiology, economics, quality improvement, editorials, Resource Corners, Practice Corners, and letters. Most clinical searches are for treatment options, with diagnosis, prognosis, and etiology being the next most frequent topics.

Abstract: Objective: To determine how well the previously validated broad and narrow Clinical Queries for treatment, diagnosis, prognosis, and etiology studies, retrieve not only primary studies but also relevant systematic reviews. Study Design and Setting: Using the Clinical Hedges Database housed at McMaster University, we tested the retrieval performance of the Clinical Queries. Results: For most purpose categories (therapy, diagnosis, prognosis, and etiology) and most databases (MEDLINE, EMBASE, CINAHL, and PsycINFO), the sensitive (broad) Clinical Queries search terms had sensitivities higher than 90% for retrieving relevant systematic reviews as well as primary studies. When testing specific (narrow) Clinical Queries, in 8 of 12 cases, specificity was 94% or higher, but sensitivity dropped below 50%. For all purpose categories and all databases, performance was improved when combining the sensitive or specific Clinical Queries with our existing sensitive or specific systematic review search filter using the Boolean OR; sensitivities ranged from 90.7% to 99.7% and specificities ranged from 92.4% to 98.0% with sensitivities higher than 50%. Conclusion: The sensitive Clinical Queries for therapy, diagnosis, prognosis, and etiology perform well in retrieving not only primary studies but also systematic reviews. Search performance can be improved by combining the Clinical Queries with our sensitive or specific systematic review filter. [Copyright &y& Elsevier]

Abstract: Objective: This study evaluated search strategies for finding high-quality studies on treatment and systematic reviews in PsycINFO. Study Design and Setting: Sixty-four journals were hand searched at McMaster University. Methodologic criteria were applied to clinically relevant articles to identify “pass” and “fail” articles; 4,985 candidate terms were compiled, 7,463 combinations for therapy articles, and 5,246 combinations for reviews. Candidate search strategy results were compared with hand searches. The proposed strategies served as “diagnostic tests” for sound studies; the hand searches were the “gold standard.” Sensitivity, specificity, precision, and accuracy were calculated. Results: Of 716 treatment articles, 233 (32.5%) met criteria for scientific merit, and 58 (11.5%) of 506 review articles met criteria for systematic reviews. For treatment studies, combined terms had a peak sensitivity of 97.9% (specificity 52.2%). Maximum specificity was 97.7% (sensitivity 51.5%). Sensitivity and specificity were each 79% when optimizing both while minimizing their difference. For review articles, combined terms had a peak sensitivity of 81.0% (specificity 54.4%). Maximum specificity was 98.1% (sensitivity 51.7%). Sensitivity and specificity were each 65% when optimizing both while minimizing their difference. Conclusions: Empirically derived search strategies can achieve high sensitivity and specificity for retrieving sound treatment studies and review articles from PsycINFO. [Copyright &y& Elsevier]

Background: Systematic reviews of health care topics are valuable summaries of all pertinent studies on focused questions. However, finding all relevant primary studies for systematic reviews remains challenging.Objectives: To determine the performance of the Clinical Queries sensitive search filter for diagnostic accuracy studies for retrieving studies for systematic reviews.Methods: We compared the yield of the sensitive Clinical Queries diagnosis search filter for MEDLINE and EMBASE to retrieve studies in diagnostic accuracy systematic reviews reported in ACP Journal Club in 2006.Results: Twelve of 22 diagnostic accuracy reviews (452 included studies) met the inclusion criteria. After excluding 11 studies not in MEDLINE or EMBASE, 95% of articles (417 of 441) were captured by the sensitive Clinical Queries diagnosis search filter (MEDLINE and EMBASE combined). Of 24 studies not retrieved by the filter, 22 were not diagnostic accuracy studies. Reanalysis of the Clinical Queries filter without these 22 nondiagnosis articles increased its performance to 99% (417 of 419). We found no substantive impact of the two articles missed by the Clinical Queries filter on the conclusions of the systematic reviews in which they were cited.Conclusion: The sensitive Clinical Queries diagnostic search filter captured 99% of articles and 100% of substantive articles indexed in MEDLINE and EMBASE in diagnostic accuracy systematic reviews. [ABSTRACT FROM AUTHOR]