The Experts below are selected from a list of 202767 Experts worldwide ranked by ideXlab platform
Amit X Garg - One of the best experts on this subject based on the ideXlab platform.
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high performance information search filters for acute kidney injury content in pubmed ovid medline and Embase
2014Co-Authors: Ainslie M Hildebrand, Nancy L Wilczynski, Brian R Haynes, Arthur V Iansavichus, Ravindra L Mehta, Chirag R Parikh, Amit X GargAbstract: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 fulltext 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 highperformance 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
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dialysis search filters for pubmed ovid medline and Embase databases
2012Co-Authors: Arthur V Iansavichus, Nancy L Wilczynski, Brian R Haynes, Salimah Z Shariff, Ann Mckibbon, Amit X Garg, Christopher W C Lee, Peter G Blake, Robert M LindsayAbstract:Summary 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.
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glomerular disease search filters for pubmed ovid medline and Embase a development and validation study
2012Co-Authors: Ainslie M Hildebrand, Nancy L Wilczynski, Brian R Haynes, Arthur V Iansavichus, Christopher W C Lee, Ann K Mckibbon, Michelle Hladunewich, William F Clark, Daniel C Cattran, Amit X GargAbstract: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.
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kidney transplantation search filters for pubmed ovid medline and Embase
2012Co-Authors: Christopher W C Lee, Nancy L Wilczynski, Brian R Haynes, Arthur V Iansavichus, Salimah Z Shariff, Ann Mckibbon, Faisal Rehman, Amit X GargAbstract:Background. Clinicians commonly search bibliographic databases such as Medline to find sound evidence to guide patient care. Unfortunately, this can be a frustrating experience because database searches often miss relevant articles. We addressed this problem for transplant professionals by developing kidney transplantation search filters for use in Medline through PubMed and Ovid Technologies, and Embase. Methods. We began by reading the full-text versions of 22,992 articles from 39 journals published across 5 years. These articles were labeled relevant to kidney transplantation or not forming our “gold standard.” We then developed close to five million kidney transplantation filters using different terms and their combinations. Afterward, these filters were applied to development and validation subsets of the articles to determine their accuracy and reliability in identifying articles with kidney transplantation content. The final kidney transplantation filters used multiple terms in combination. Results. The best performing filters achieved 97.5% sensitivity (95% confidence interval, 96.4%–98.5%), and 98.0% specificity (95% confidence interval, 97.8%–98.3%). Similar high performance was achieved for filters developed for Ovid Medline and Embase. Proof-of-concept searches confirmed more relevant articles are retrieved using these filters. Conclusions. These kidney transplantation filters can now be used in Medline and Embase databases to improve clinician searching.
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optimal search filters for renal information in Embase
2010Co-Authors: Arthur V Iansavichus, Nancy L Wilczynski, Brian R Haynes, Salimah Z Shariff, Matthew A Weir, Ann Mckibbon, Faisal Rehman, Amit X GargAbstract: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.
Brian R Haynes - One of the best experts on this subject based on the ideXlab platform.
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high performance information search filters for ckd content in pubmed ovid medline and Embase
2015Co-Authors: Arthur V Iansavichus, Nancy L Wilczynski, Brian R Haynes, Ainslie M Hildebrand, Adeera Levin, Brenda R Hemmelgarn, Gihad Nesrallah, Danielle M NashAbstract:Background Finding relevant articles in large bibliographic databases such as PubMed, Ovid MEDLINE, and Embase to inform care and future research is challenging. Articles relevant to chronic kidney disease (CKD) are particularly difficult to find because they are often published under different terminology and are found across a wide range of journal types. Study Design We used computer automation within a diagnostic test assessment framework to develop and validate information search filters to identify CKD articles in large bibliographic databases. Setting & Participants 22,992 full-text articles in PubMed, Ovid MEDLINE, or Embase. Index Test 1,374,148 unique search filters. Reference Test We established the reference standard of article relevance to CKD by manual review of all full-text articles using prespecified criteria to determine whether each article contained CKD content or not. We then assessed filter performance by calculating sensitivity, specificity, and positive predictive value for the retrieval of CKD articles. Filters with high sensitivity and specificity for the identification of CKD articles in the development phase (two-thirds of the sample) were then retested in the validation phase (remaining one-third of the sample). Results We developed and validated high-performance CKD search filters for each bibliographic database. Filters optimized for sensitivity reached at least 99% sensitivity, and filters optimized for specificity reached at least 97% specificity. The filters were complex; for example, one PubMed filter included more than 89 terms used in combination, including "chronic kidney disease," "renal insufficiency," and "renal fibrosis." In proof-of-concept searches, physicians found more articles relevant to the topic of CKD with the use of these filters. Limitations As knowledge of the pathogenesis of CKD grows and definitions change, these filters will need to be updated to incorporate new terminology used to index relevant articles. Conclusions PubMed, Ovid MEDLINE, and Embase can be filtered reliably for articles relevant to CKD. These high-performance information filters are now available online and can be used to better identify CKD content in large bibliographic databases.
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high performance information search filters for acute kidney injury content in pubmed ovid medline and Embase
2014Co-Authors: Ainslie M Hildebrand, Nancy L Wilczynski, Brian R Haynes, Arthur V Iansavichus, Ravindra L Mehta, Chirag R Parikh, Amit X GargAbstract: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 fulltext 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 highperformance 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
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dialysis search filters for pubmed ovid medline and Embase databases
2012Co-Authors: Arthur V Iansavichus, Nancy L Wilczynski, Brian R Haynes, Salimah Z Shariff, Ann Mckibbon, Amit X Garg, Christopher W C Lee, Peter G Blake, Robert M LindsayAbstract:Summary 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.
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glomerular disease search filters for pubmed ovid medline and Embase a development and validation study
2012Co-Authors: Ainslie M Hildebrand, Nancy L Wilczynski, Brian R Haynes, Arthur V Iansavichus, Christopher W C Lee, Ann K Mckibbon, Michelle Hladunewich, William F Clark, Daniel C Cattran, Amit X GargAbstract: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.
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kidney transplantation search filters for pubmed ovid medline and Embase
2012Co-Authors: Christopher W C Lee, Nancy L Wilczynski, Brian R Haynes, Arthur V Iansavichus, Salimah Z Shariff, Ann Mckibbon, Faisal Rehman, Amit X GargAbstract:Background. Clinicians commonly search bibliographic databases such as Medline to find sound evidence to guide patient care. Unfortunately, this can be a frustrating experience because database searches often miss relevant articles. We addressed this problem for transplant professionals by developing kidney transplantation search filters for use in Medline through PubMed and Ovid Technologies, and Embase. Methods. We began by reading the full-text versions of 22,992 articles from 39 journals published across 5 years. These articles were labeled relevant to kidney transplantation or not forming our “gold standard.” We then developed close to five million kidney transplantation filters using different terms and their combinations. Afterward, these filters were applied to development and validation subsets of the articles to determine their accuracy and reliability in identifying articles with kidney transplantation content. The final kidney transplantation filters used multiple terms in combination. Results. The best performing filters achieved 97.5% sensitivity (95% confidence interval, 96.4%–98.5%), and 98.0% specificity (95% confidence interval, 97.8%–98.3%). Similar high performance was achieved for filters developed for Ovid Medline and Embase. Proof-of-concept searches confirmed more relevant articles are retrieved using these filters. Conclusions. These kidney transplantation filters can now be used in Medline and Embase databases to improve clinician searching.
Nancy L Wilczynski - One of the best experts on this subject based on the ideXlab platform.
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high performance information search filters for ckd content in pubmed ovid medline and Embase
2015Co-Authors: Arthur V Iansavichus, Nancy L Wilczynski, Brian R Haynes, Ainslie M Hildebrand, Adeera Levin, Brenda R Hemmelgarn, Gihad Nesrallah, Danielle M NashAbstract:Background Finding relevant articles in large bibliographic databases such as PubMed, Ovid MEDLINE, and Embase to inform care and future research is challenging. Articles relevant to chronic kidney disease (CKD) are particularly difficult to find because they are often published under different terminology and are found across a wide range of journal types. Study Design We used computer automation within a diagnostic test assessment framework to develop and validate information search filters to identify CKD articles in large bibliographic databases. Setting & Participants 22,992 full-text articles in PubMed, Ovid MEDLINE, or Embase. Index Test 1,374,148 unique search filters. Reference Test We established the reference standard of article relevance to CKD by manual review of all full-text articles using prespecified criteria to determine whether each article contained CKD content or not. We then assessed filter performance by calculating sensitivity, specificity, and positive predictive value for the retrieval of CKD articles. Filters with high sensitivity and specificity for the identification of CKD articles in the development phase (two-thirds of the sample) were then retested in the validation phase (remaining one-third of the sample). Results We developed and validated high-performance CKD search filters for each bibliographic database. Filters optimized for sensitivity reached at least 99% sensitivity, and filters optimized for specificity reached at least 97% specificity. The filters were complex; for example, one PubMed filter included more than 89 terms used in combination, including "chronic kidney disease," "renal insufficiency," and "renal fibrosis." In proof-of-concept searches, physicians found more articles relevant to the topic of CKD with the use of these filters. Limitations As knowledge of the pathogenesis of CKD grows and definitions change, these filters will need to be updated to incorporate new terminology used to index relevant articles. Conclusions PubMed, Ovid MEDLINE, and Embase can be filtered reliably for articles relevant to CKD. These high-performance information filters are now available online and can be used to better identify CKD content in large bibliographic databases.
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high performance information search filters for acute kidney injury content in pubmed ovid medline and Embase
2014Co-Authors: Ainslie M Hildebrand, Nancy L Wilczynski, Brian R Haynes, Arthur V Iansavichus, Ravindra L Mehta, Chirag R Parikh, Amit X GargAbstract: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 fulltext 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 highperformance 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
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dialysis search filters for pubmed ovid medline and Embase databases
2012Co-Authors: Arthur V Iansavichus, Nancy L Wilczynski, Brian R Haynes, Salimah Z Shariff, Ann Mckibbon, Amit X Garg, Christopher W C Lee, Peter G Blake, Robert M LindsayAbstract:Summary 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.
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glomerular disease search filters for pubmed ovid medline and Embase a development and validation study
2012Co-Authors: Ainslie M Hildebrand, Nancy L Wilczynski, Brian R Haynes, Arthur V Iansavichus, Christopher W C Lee, Ann K Mckibbon, Michelle Hladunewich, William F Clark, Daniel C Cattran, Amit X GargAbstract: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.
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kidney transplantation search filters for pubmed ovid medline and Embase
2012Co-Authors: Christopher W C Lee, Nancy L Wilczynski, Brian R Haynes, Arthur V Iansavichus, Salimah Z Shariff, Ann Mckibbon, Faisal Rehman, Amit X GargAbstract:Background. Clinicians commonly search bibliographic databases such as Medline to find sound evidence to guide patient care. Unfortunately, this can be a frustrating experience because database searches often miss relevant articles. We addressed this problem for transplant professionals by developing kidney transplantation search filters for use in Medline through PubMed and Ovid Technologies, and Embase. Methods. We began by reading the full-text versions of 22,992 articles from 39 journals published across 5 years. These articles were labeled relevant to kidney transplantation or not forming our “gold standard.” We then developed close to five million kidney transplantation filters using different terms and their combinations. Afterward, these filters were applied to development and validation subsets of the articles to determine their accuracy and reliability in identifying articles with kidney transplantation content. The final kidney transplantation filters used multiple terms in combination. Results. The best performing filters achieved 97.5% sensitivity (95% confidence interval, 96.4%–98.5%), and 98.0% specificity (95% confidence interval, 97.8%–98.3%). Similar high performance was achieved for filters developed for Ovid Medline and Embase. Proof-of-concept searches confirmed more relevant articles are retrieved using these filters. Conclusions. These kidney transplantation filters can now be used in Medline and Embase databases to improve clinician searching.
Arthur V Iansavichus - One of the best experts on this subject based on the ideXlab platform.
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high performance information search filters for ckd content in pubmed ovid medline and Embase
2015Co-Authors: Arthur V Iansavichus, Nancy L Wilczynski, Brian R Haynes, Ainslie M Hildebrand, Adeera Levin, Brenda R Hemmelgarn, Gihad Nesrallah, Danielle M NashAbstract:Background Finding relevant articles in large bibliographic databases such as PubMed, Ovid MEDLINE, and Embase to inform care and future research is challenging. Articles relevant to chronic kidney disease (CKD) are particularly difficult to find because they are often published under different terminology and are found across a wide range of journal types. Study Design We used computer automation within a diagnostic test assessment framework to develop and validate information search filters to identify CKD articles in large bibliographic databases. Setting & Participants 22,992 full-text articles in PubMed, Ovid MEDLINE, or Embase. Index Test 1,374,148 unique search filters. Reference Test We established the reference standard of article relevance to CKD by manual review of all full-text articles using prespecified criteria to determine whether each article contained CKD content or not. We then assessed filter performance by calculating sensitivity, specificity, and positive predictive value for the retrieval of CKD articles. Filters with high sensitivity and specificity for the identification of CKD articles in the development phase (two-thirds of the sample) were then retested in the validation phase (remaining one-third of the sample). Results We developed and validated high-performance CKD search filters for each bibliographic database. Filters optimized for sensitivity reached at least 99% sensitivity, and filters optimized for specificity reached at least 97% specificity. The filters were complex; for example, one PubMed filter included more than 89 terms used in combination, including "chronic kidney disease," "renal insufficiency," and "renal fibrosis." In proof-of-concept searches, physicians found more articles relevant to the topic of CKD with the use of these filters. Limitations As knowledge of the pathogenesis of CKD grows and definitions change, these filters will need to be updated to incorporate new terminology used to index relevant articles. Conclusions PubMed, Ovid MEDLINE, and Embase can be filtered reliably for articles relevant to CKD. These high-performance information filters are now available online and can be used to better identify CKD content in large bibliographic databases.
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high performance information search filters for acute kidney injury content in pubmed ovid medline and Embase
2014Co-Authors: Ainslie M Hildebrand, Nancy L Wilczynski, Brian R Haynes, Arthur V Iansavichus, Ravindra L Mehta, Chirag R Parikh, Amit X GargAbstract: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 fulltext 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 highperformance 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
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dialysis search filters for pubmed ovid medline and Embase databases
2012Co-Authors: Arthur V Iansavichus, Nancy L Wilczynski, Brian R Haynes, Salimah Z Shariff, Ann Mckibbon, Amit X Garg, Christopher W C Lee, Peter G Blake, Robert M LindsayAbstract:Summary 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.
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glomerular disease search filters for pubmed ovid medline and Embase a development and validation study
2012Co-Authors: Ainslie M Hildebrand, Nancy L Wilczynski, Brian R Haynes, Arthur V Iansavichus, Christopher W C Lee, Ann K Mckibbon, Michelle Hladunewich, William F Clark, Daniel C Cattran, Amit X GargAbstract: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.
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kidney transplantation search filters for pubmed ovid medline and Embase
2012Co-Authors: Christopher W C Lee, Nancy L Wilczynski, Brian R Haynes, Arthur V Iansavichus, Salimah Z Shariff, Ann Mckibbon, Faisal Rehman, Amit X GargAbstract:Background. Clinicians commonly search bibliographic databases such as Medline to find sound evidence to guide patient care. Unfortunately, this can be a frustrating experience because database searches often miss relevant articles. We addressed this problem for transplant professionals by developing kidney transplantation search filters for use in Medline through PubMed and Ovid Technologies, and Embase. Methods. We began by reading the full-text versions of 22,992 articles from 39 journals published across 5 years. These articles were labeled relevant to kidney transplantation or not forming our “gold standard.” We then developed close to five million kidney transplantation filters using different terms and their combinations. Afterward, these filters were applied to development and validation subsets of the articles to determine their accuracy and reliability in identifying articles with kidney transplantation content. The final kidney transplantation filters used multiple terms in combination. Results. The best performing filters achieved 97.5% sensitivity (95% confidence interval, 96.4%–98.5%), and 98.0% specificity (95% confidence interval, 97.8%–98.3%). Similar high performance was achieved for filters developed for Ovid Medline and Embase. Proof-of-concept searches confirmed more relevant articles are retrieved using these filters. Conclusions. These kidney transplantation filters can now be used in Medline and Embase databases to improve clinician searching.
Yoon K Loke - One of the best experts on this subject based on the ideXlab platform.
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the development of search filters for adverse effects of medical devices in medline and Embase
2019Co-Authors: Susan Pamela Golder, Kath Wright, Kelly Farrah, Monika Mierzwinskiurban, Yoon K LokeAbstract:Background: Objectively derived search filters for adverse drug effects and complications in surgery have been developed but not for medical device adverse effects. Objective: To develop and validate search filters to retrieve evidence on medical device adverse effects from ovid medline and Embase. Methods: We identified systematic reviews from Epistemonikos and the Health Technology Assessment (hta) database. Included studies within these reviews that reported on medical device adverse effects were randomly divided into three test sets and one validation set of records. Using word frequency analysis from one test set, we constructed a sensitivity maximising search strategy. This strategy was refined using two other test sets, then validated. Results: From 186 systematic reviews which met our inclusion criteria, 1984 unique included studies were available from medline and 1986 from Embase. Generic adverse effects searches in medline and Embase achieved 84% and 83% sensitivity. Recall was improved to over 90%, however, when specific adverse effects terms were added. Conclusion: We have derived and validated novel search filters that retrieve over 80% of records with medical device adverse effects data in medline and Embase. The addition of specific adverse effects terms is required to achieve higher levels of sensitivity.
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The development of search filters for adverse effects of surgical interventions in MEDLINE and Embase
2018Co-Authors: Susan Pamela Golder, Kath Wright, Yoon K LokeAbstract:Background: Search filter development for adverse effects has tended to focus on retrieving studies of drug interventions. However, a different approach is required for surgical interventions. Objective: To develop and validate search filters for medline and Embase for the adverse effects of surgical interventions. Methods: Systematic reviews of surgical interventions where the primary focus was to evaluate adverse effect(s) were sought. The included studies within these reviews were divided randomly into a development set, evaluation set and validation set. Using word frequency analysis we constructed a sensitivity maximising search strategy and this was tested in the evaluation and validation set. Results: Three hundred and fifty eight papers were included from 19 surgical intervention reviews. Three hundred and fifty two papers were available on medline and 348 were available on Embase. Generic adverse effects search strategies in medline and Embase could achieve approximately 90% relative recall. Recall could be further improved with the addition of specific adverse effects terms to the search strategies. Conclusion: We have derived and validated a novel search filter that has reasonable performance for identifying adverse effects of surgical interventions in medline and Embase. However, we appreciate the limitations of our methods, and recommend further research on larger sample sizes and prospective systematic reviews.
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the feasibility of a search filter for the adverse effects of nondrug interventions in medline and Embase
2017Co-Authors: Susan Pamela Golder, Kath Wright, Yoon K LokeAbstract:Authors and indexers are increasingly including terms for adverse drug effects in the titles, abstracts, or indexing of records in MEDLINE and Embase. However, it is not clear if this is the same for studies with nondrug adverse effects data. We therefore assessed the feasibility of using adverse effects terms when searching MEDLINE or Embase to retrieve papers of nondrug adverse effects. A collection of papers that reported data on nondrug adverse effects was sought from included studies of systematic reviews. Each included study was analysed to ascertain whether the corresponding record in MEDLINE and Embase included adverse effects terms in the title, abstract, or indexing. From 9129 records screened from DARE, 30 reviews evaluating nondrug adverse effects met our inclusion criteria. From these, 635 unique papers were included in our analysis. Sensitive searches for adverse effects required generic and specific named adverse effects terms using the title, abstract, and indexing. Records relating to surgical interventions were more likely to contain adverse effects terms than records relating to nonsurgical interventions. Using any adverse effects terms in the title, abstract or indexing in MEDLINE and Embase would have identified an average of 94% of papers on surgical adverse effect interventions per systematic review and 72% of papers on nonsurgical adverse effects per systematic review. Hence, while a generic nondrug adverse effect search filter may not yet be feasible, a filter for the adverse effects of surgical interventions may be within reach.
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the performance of adverse effects search filters in medline and Embase
2012Co-Authors: Su Golder, Yoon K LokeAbstract:Background: Search filters can potentially improve the efficiency of searches involving electronic databases such as medline and Embase. Although search filters have been developed for identifying records that contain adverse effects data, little is known about the sensitivity of such filters. Objectives: This study measured the sensitivity of using available adverse effects filters to retrieve papers with adverse effects data. Methods: A total of 233 included studies from 26 systematic reviews of adverse effects were used for analysis. Search filters from medline and Embase were tested for their sensitivity in retrieving the records included in these reviews. In addition, the sensitivity of each individual search term used in at least one search filter was measured. Results: Subheadings proved the most useful search terms in both medline and Embase. No indexing terms in medline achieved over 12% sensitivity. The sensitivity of published search filters varied in medline from 3% to 93% and in Embase from 57% to 97%. Whether this level of sensitivity is acceptable will be dependent on the purpose of the search. Conclusions: Although no adverse effects search filter captured all the relevant records, high sensitivity could be achieved. Search filters may therefore be useful in retrieving adverse effects data.
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sensitivity and precision of adverse effects search filters in medline and Embase a case study of fractures with thiazolidinediones
2012Co-Authors: Su Golder, Yoon K LokeAbstract:Background: Search filters have been developed in MEDLINE and Embase to help overcome the challenges of searching electronic databases for information on adverse effects. However, little evaluation of their effectiveness has been carried out. Objectives: To measure the sensitivity and precision of available adverse effects search filters in MEDLINE and Embase. Methods: A case study systematic review of fracture related adverse effects associated with the use of thiazolidinediones was used. Twelve MEDLINE search strategies and three Embase search strategies were tested. Results: Nineteen relevant references from MEDLINE and 24 from Embase were included in the review. Four search filters in MEDLINE achieved high sensitivity (95 or 100%) with an improved level of precision from searches without any adverse effects filter. High precision in MEDLINE could also be achieved (up to 53%) using search filters that rely on Medical Subject Headings. No search filter in Embase achieved high precision (all were under 5%) and the highest sensitivity in Embase was 83%. Conclusions: Adverse effects search filters appear to be effective in MEDLINE for achieving either high sensitivity or high precision. Search filters in Embase, however, do not appear as effective, particularly in improving precision.
