Dataset Information

Search results outliers among MEDLINE platforms.

ABSTRACT: Objective:Hypothetically, content in MEDLINE records is consistent across multiple platforms. Though platforms have different interfaces and requirements for query syntax, results should be similar when the syntax is controlled for across the platforms. The authors investigated how search result counts varied when searching records among five MEDLINE platforms. Methods:We created 29 sets of search queries targeting various metadata fields and operators. Within search sets, we adapted 5 distinct, compatible queries to search 5 MEDLINE platforms (PubMed, ProQuest, EBSCOhost, Web of Science, and Ovid), totaling 145 final queries. The 5 queries were designed to be logically and semantically equivalent and were modified only to match platform syntax requirements. We analyzed the result counts and compared PubMed's MEDLINE result counts to result counts from the other platforms. We identified outliers by measuring the result count deviations using modified z-scores centered around PubMed's MEDLINE results. Results:Web of Science and ProQuest searches were the most likely to deviate from the equivalent PubMed searches. EBSCOhost and Ovid were less likely to deviate from PubMed searches. Ovid's results were the most consistent with PubMed's but appeared to apply an indexing algorithm that resulted in lower retrieval sets among equivalent searches in PubMed. Web of Science exhibited problems with exploding or not exploding Medical Subject Headings (MeSH) terms. Conclusion:Platform enhancements among interfaces affect record retrieval and challenge the expectation that MEDLINE platforms should, by default, be treated as MEDLINE. Substantial inconsistencies in search result counts, as demonstrated here, should raise concerns about the impact of platform-specific influences on search results.

SUBMITTER: Burns CS

PROVIDER: S-EPMC6579582 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

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Search results outliers among MEDLINE platforms.

Burns Christopher Sean CS Shapiro Robert M RM Nix Tyler T Huber Jeffrey T JT

Journal of the Medical Library Association : JMLA 20190701 3

<h4>Objective</h4>Hypothetically, content in MEDLINE records is consistent across multiple platforms. Though platforms have different interfaces and requirements for query syntax, results should be similar when the syntax is controlled for across the platforms. The authors investigated how search result counts varied when searching records among five MEDLINE platforms.<h4>Methods</h4>We created 29 sets of search queries targeting various metadata fields and operators. Within search sets, we adap ...[more]

PMID: 31258442

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Project description:BackgroundDegenerative cervical myelopathy (DCM) is a common condition with many unmet clinical needs. Pooled analysis of studies is an important tool for advancing medical understanding. This process starts with a systematic search of the literature. Identification of studies in DCM is challenged by a number of factors, including non-specific terminology and index terms. Search filters or HEDGEs, are search strings developed and validated to optimise medical literature searches. We aimed to develop a search filter for DCM for the MEDLINE database.MethodsThe diagnostic test assessment framework of a "development dataset" and seperate "validation dataset" was used. The development dataset was formed by hand searching four leading spinal journals (Spine, Journal of Neurosurgery Spine, Spinal Cord and Journal of Spinal Disorders and Techniques) in 2005 and 2010. The search filter was initially developed focusing on sensitivity and subsequently refined using NOT functions to improve specificity. One validation dataset was formed from DCM narrative and systematic review articles and the second, articles published in April of 1989, 1993, 1997, 2001, 2005, 2009, 2013 and 2017 retrieved via the search MeSH term 'Spine'. Metrics of sensitivity, specificity, precision and accuracy were used to test performance.ResultsHand searching identified 77/1094 relevant articles for 2005 and 55/1199 for 2010. We developed a search hedge with 100% sensitivity and a precision of 30 and 29% for the 2005 and 2010 development datasets respectively. For the selected time periods, EXP Spine returned 2113 publications and 30 were considered relevant. The search filter identified all 30 relevant articles, with a specificity of 94% and precision of 20%. Of the 255 references listed in the narrative index reviews, 225 were indexed in MEDLINE and 165 (73%) were relevant articles. All relevant articles were identified and accuracy ranged from 67 to 97% over the three reviews. Of the 42 articles returned from 3 recent systematic reviews, all were identified by the filter.ConclusionsWe have developed a highly sensitive hedge for the research of DCM. Whilst precision is similarly low as other hedges, this search filter can be used as an adjunct for DCM search strategies.

Project description:BackgroundReceiving extraneous articles in response to a query submitted to MEDLINE/PubMed is common. When submitting a multi-word query (which is the majority of queries submitted), the presence of all query words within each article may be a necessary condition for retrieving relevant articles, but not sufficient. Ideally a relationship between the query words in the article is also required. We propose that if two words occur within an article, the probability that a relation between them is explained is higher when the words occur within adjacent sentences versus remote sentences. Therefore, sentence-level concurrence can be used as a surrogate for existence of the relationship between the words. In order to avoid the irrelevant articles, one solution would be to increase the search specificity. Another solution is to estimate a relevance score to sort the retrieved articles. However among the >30 retrieval services available for MEDLINE, only a few estimate a relevance score, and none detects and incorporates the relation between the query words as part of the relevance score.ResultsWe have developed "Relemed", a search engine for MEDLINE. Relemed increases specificity and precision of retrieval by searching for query words within sentences rather than the whole article. It uses sentence-level concurrence as a statistical surrogate for the existence of relationship between the words. It also estimates a relevance score and sorts the results on this basis, thus shifting irrelevant articles lower down the list. In two case studies, we demonstrate that the most relevant articles appear at the top of the Relemed results, while this is not necessarily the case with a PubMed search. We have also shown that a Relemed search includes not only all the articles retrieved by PubMed, but potentially additional relevant articles, due to the extended 'automatic term mapping' and text-word searching features implemented in Relemed.ConclusionBy using sentence-level matching, Relemed can deliver higher specificity, thus eliminating more false-positive articles. By introducing an appropriate relevance metric, the most relevant articles on which the user wishes to focus are listed first. Relemed also shrinks the displayed text, and hence the time spent scanning the articles.

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Search results outliers among MEDLINE platforms.

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Search results outliers among MEDLINE platforms.

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