Limits...
Filtering Medline for a clinical discipline: diagnostic test assessment framework.

Garg AX, Iansavichus AV, Wilczynski NL, Kastner M, Baier LA, Shariff SZ, Rehman F, Weir M, McKibbon KA, Haynes RB - BMJ (2009)

Bottom Line: The performance of 1 155 087 unique renal filters was compared with the manual review.Storing these high performance renal filters in PubMed could help clinicians with their everyday searching.Filters can also be developed for other clinical disciplines by using similar methods.

View Article: PubMed Central - PubMed

Affiliation: Division of Nephrology, University of Western Ontario, London, ON, Canada N6A 5C1. amit.garg@lhsc.on.ca

ABSTRACT

Objective: To develop and test a Medline filter that allows clinicians to search for articles within a clinical discipline, rather than searching the entire Medline database.

Design: Diagnostic test assessment framework with development and validation phases.

Setting: Sample of 4657 articles published in 2006 from 40 journals. Reviews Each article was manually reviewed, and 19.8% contained information relevant to the discipline of nephrology. The performance of 1 155 087 unique renal filters was compared with the manual review.

Main outcome measures: Sensitivity, specificity, precision, and accuracy of each filter.

Results: The best renal filters combined two to 14 terms or phrases and included the terms "kidney" with multiple endings (that is, truncation), "renal replacement therapy", "renal dialysis", "kidney function tests", "renal", "nephr" truncated, "glomerul" truncated, and "proteinuria". These filters achieved peak sensitivities of 97.8% and specificities of 98.5%. Performance of filters remained excellent in the validation phase.

Conclusions: Medline can be filtered for the discipline of nephrology in a reliable manner. Storing these high performance renal filters in PubMed could help clinicians with their everyday searching. Filters can also be developed for other clinical disciplines by using similar methods.

Show MeSH

Related in: MedlinePlus

Fig 3 Data collection and filter development
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
getmorefigures.php?uid=PMC2746885&req=5

fig3: Fig 3 Data collection and filter development

Mentions: We used a diagnostic test assessment framework with development and validation phases (fig 3, table 1). We divided a sample of articles from all available articles in Medline into two sets: a development dataset and a validation dataset. We produced a “reference standard” by manually reviewing a sample of articles to determine whether they contained any type of renal information. We then compared the retrieval performance of various filters made up of individual search terms and combinations of terms with the reference standard of manual review. We treated each filter as a “diagnostic test” for the identification (retrieval) of renal articles. For each filter, we constructed a two by two contingency table and quantified agreement (measures outlined in table 1). We then examined in the validation set of articles those filters that performed well in the development set of articles.


Filtering Medline for a clinical discipline: diagnostic test assessment framework.

Garg AX, Iansavichus AV, Wilczynski NL, Kastner M, Baier LA, Shariff SZ, Rehman F, Weir M, McKibbon KA, Haynes RB - BMJ (2009)

Fig 3 Data collection and filter development
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC2746885&req=5

fig3: Fig 3 Data collection and filter development
Mentions: We used a diagnostic test assessment framework with development and validation phases (fig 3, table 1). We divided a sample of articles from all available articles in Medline into two sets: a development dataset and a validation dataset. We produced a “reference standard” by manually reviewing a sample of articles to determine whether they contained any type of renal information. We then compared the retrieval performance of various filters made up of individual search terms and combinations of terms with the reference standard of manual review. We treated each filter as a “diagnostic test” for the identification (retrieval) of renal articles. For each filter, we constructed a two by two contingency table and quantified agreement (measures outlined in table 1). We then examined in the validation set of articles those filters that performed well in the development set of articles.

Bottom Line: The performance of 1 155 087 unique renal filters was compared with the manual review.Storing these high performance renal filters in PubMed could help clinicians with their everyday searching.Filters can also be developed for other clinical disciplines by using similar methods.

View Article: PubMed Central - PubMed

Affiliation: Division of Nephrology, University of Western Ontario, London, ON, Canada N6A 5C1. amit.garg@lhsc.on.ca

ABSTRACT

Objective: To develop and test a Medline filter that allows clinicians to search for articles within a clinical discipline, rather than searching the entire Medline database.

Design: Diagnostic test assessment framework with development and validation phases.

Setting: Sample of 4657 articles published in 2006 from 40 journals. Reviews Each article was manually reviewed, and 19.8% contained information relevant to the discipline of nephrology. The performance of 1 155 087 unique renal filters was compared with the manual review.

Main outcome measures: Sensitivity, specificity, precision, and accuracy of each filter.

Results: The best renal filters combined two to 14 terms or phrases and included the terms "kidney" with multiple endings (that is, truncation), "renal replacement therapy", "renal dialysis", "kidney function tests", "renal", "nephr" truncated, "glomerul" truncated, and "proteinuria". These filters achieved peak sensitivities of 97.8% and specificities of 98.5%. Performance of filters remained excellent in the validation phase.

Conclusions: Medline can be filtered for the discipline of nephrology in a reliable manner. Storing these high performance renal filters in PubMed could help clinicians with their everyday searching. Filters can also be developed for other clinical disciplines by using similar methods.

Show MeSH
Related in: MedlinePlus