Comparing test searches in PubMed and Google Scholar.
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ABSTRACT:
SUBMITTER: Shultz M
PROVIDER: S-EPMC2000776 | biostudies-literature | 2007 Oct
REPOSITORIES: biostudies-literature
Publications
Comparing test searches in PubMed and Google Scholar.
Journal of the Medical Library Association : JMLA 20071001 4
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Project description:Physicians frequently search PubMed for information to guide patient care. More recently, Google Scholar has gained popularity as another freely accessible bibliographic database.To compare the performance of searches in PubMed and Google Scholar.We surveyed nephrologists (kidney specialists) and provided each with a unique clinical question derived from 100 renal therapy systematic reviews. Each physician provided the search terms they would type into a bibliographic database to locate evidence to answer the clinical question. We executed each of these searches in PubMed and Google Scholar and compared results for the first 40 records retrieved (equivalent to 2 default search pages in PubMed). We evaluated the recall (proportion of relevant articles found) and precision (ratio of relevant to nonrelevant articles) of the searches performed in PubMed and Google Scholar. Primary studies included in the systematic reviews served as the reference standard for relevant articles. We further documented whether relevant articles were available as free full-texts.Compared with PubMed, the average search in Google Scholar retrieved twice as many relevant articles (PubMed: 11%; Google Scholar: 22%; P<.001). Precision was similar in both databases (PubMed: 6%; Google Scholar: 8%; P=.07). Google Scholar provided significantly greater access to free full-text publications (PubMed: 5%; Google Scholar: 14%; P<.001).For quick clinical searches, Google Scholar returns twice as many relevant articles as PubMed and provides greater access to free full-text articles.
| S-EPMC3757915 | biostudies-literature
Project description:BACKGROUND:The usefulness of Google Scholar (GS) as a bibliographic database for biomedical systematic review (SR) searching is a subject of current interest and debate in research circles. Recent research has suggested GS might even be used alone in SR searching. This assertion is challenged here by testing whether GS can locate all studies included in 21 previously published SRs. Second, it examines the recall of GS, taking into account the maximum number of items that can be viewed, and tests whether more complete searches created by an information specialist will improve recall compared to the searches used in the 21 published SRs. METHODS:The authors identified 21 biomedical SRs that had used GS and PubMed as information sources and reported their use of identical, reproducible search strategies in both databases. These search strategies were rerun in GS and PubMed, and analyzed as to their coverage and recall. Efforts were made to improve searches that underperformed in each database. RESULTS:GS' overall coverage was higher than PubMed (98% versus 91%) and overall recall is higher in GS: 80% of the references included in the 21 SRs were returned by the original searches in GS versus 68% in PubMed. Only 72% of the included references could be used as they were listed among the first 1,000 hits (the maximum number shown). Practical precision (the number of included references retrieved in the first 1,000, divided by 1,000) was on average 1.9%, which is only slightly lower than in other published SRs. Improving searches with the lowest recall resulted in an increase in recall from 48% to 66% in GS and, in PubMed, from 60% to 85%. CONCLUSIONS:Although its coverage and precision are acceptable, GS, because of its incomplete recall, should not be used as a single source in SR searching. A specialized, curated medical database such as PubMed provides experienced searchers with tools and functionality that help improve recall, and numerous options in order to optimize precision. Searches for SRs should be performed by experienced searchers creating searches that maximize recall for as many databases as deemed necessary by the search expert.
| S-EPMC3882110 | biostudies-literature
Project description:We read with considerable interest the study by Gusenbauer and Haddaway (Gusenbauer and Haddaway, 2020, Research Synthesis Methods, doi:10.1002/jrsm.1378) comparing the systematic search qualities of 28 search systems, including Google Scholar (GS) and PubMed. Google Scholar and PubMed are the two most popular free academic search tools in biology and chemistry, with GS being the number one search tool in the world. Those academics using GS as their principal system for literature searches may be unaware of research which enumerates five critical features for scientific literature tools that greatly influenced Gusenbauer's 2020 study. Using this list as the framework for a targeted comparison between just GS and PubMed, we found stark differences which overwhelmingly favored PubMed. In this comment, we show that by comparing the characteristics of the two search tools, features that are particularly useful in one search tool, but are missing in the other, are strikingly spotlighted. One especially popular feature that ubiquitously appears in GS, but not in PubMed, is the forward citation search found under every citation as a clickable Cited by N link. We seek to improve the PubMed search experience using two approaches. First, we request that PubMed add Cited by N links, making them as omnipresent as the GS links. Second, we created an open-source command-line tool, pmidcite, which is used alongside PubMed to give information to researchers to help with the choice of the next paper to examine, analogous to how GS's Cited by N links help to guide users. Find pmidcite at https://github.com/dvklopfenstein/pmidcite.
| S-EPMC7984402 | biostudies-literature
Project description:BackgroundWithin the context of a European network dedicated to the study of sarcoma the relevant literature on sarcoma risk factors was collected by searching PubMed and Google Scholar, the two information storage and retrieval databases which can be accessed without charge. The present study aims to appraise the relative proficiency of PubMed and Google Scholar.FindingsUnlike PubMed, Google Scholar does not allow a choice between "Human" and "Animal" studies, nor between "Classical" and other types of studies. As a result, searches with Google Scholar produced high numbers of citations that have to be filtered. Google Scholar resulted in a higher sensitivity (proportion of relevant articles, meeting the search criteria), while PubMed in a higher specificity (proportion of lower quality articles not meeting the criteria, that are not retrieved). Concordance between Google Scholar and PubMed was as low as 8%.ConclusionsThis study focused just on one topic. Although further studies are warranted, PM and GS appear to be complementary and their integration could greatly improve the search of references in medical research.
| S-EPMC2874568 | biostudies-literature
Project description:BackgroundRecent research indicates a high recall in Google Scholar searches for systematic reviews. These reports raised high expectations of Google Scholar as a unified and easy to use search interface. However, studies on the coverage of Google Scholar rarely used the search interface in a realistic approach but instead merely checked for the existence of gold standard references. In addition, the severe limitations of the Google Search interface must be taken into consideration when comparing with professional literature retrieval tools.The objectives of this work are to measure the relative recall and precision of searches with Google Scholar under conditions which are derived from structured search procedures conventional in scientific literature retrieval; and to provide an overview of current advantages and disadvantages of the Google Scholar search interface in scientific literature retrieval.MethodsGeneral and MEDLINE-specific search strategies were retrieved from 14 Cochrane systematic reviews. Cochrane systematic review search strategies were translated to Google Scholar search expression as good as possible under consideration of the original search semantics. The references of the included studies from the Cochrane reviews were checked for their inclusion in the result sets of the Google Scholar searches. Relative recall and precision were calculated.ResultsWe investigated Cochrane reviews with a number of included references between 11 and 70 with a total of 396 references. The Google Scholar searches resulted in sets between 4,320 and 67,800 and a total of 291,190 hits. The relative recall of the Google Scholar searches had a minimum of 76.2% and a maximum of 100% (7 searches). The precision of the Google Scholar searches had a minimum of 0.05% and a maximum of 0.92%. The overall relative recall for all searches was 92.9%, the overall precision was 0.13%.ConclusionThe reported relative recall must be interpreted with care. It is a quality indicator of Google Scholar confined to an experimental setting which is unavailable in systematic retrieval due to the severe limitations of the Google Scholar search interface. Currently, Google Scholar does not provide necessary elements for systematic scientific literature retrieval such as tools for incremental query optimization, export of a large number of references, a visual search builder or a history function. Google Scholar is not ready as a professional searching tool for tasks where structured retrieval methodology is necessary.
| S-EPMC3840556 | biostudies-literature
Project description:Google Scholar (GS) is a free tool that may be used by researchers to analyze citations; find appropriate literature; or evaluate the quality of an author or a contender for tenure, promotion, a faculty position, funding, or research grants. GS has become a major bibliographic and citation database. For assessing the literature, databases, such as PubMed, PsycINFO, Scopus, and Web of Science, can be used in place of GS because they are more reliable. The aim of this study was to examine the accuracy of citation data collected from GS and provide a comprehensive description of the errors and miscounts identified. For this purpose, 281 documents that cited 2 specific works were retrieved via Publish or Perish software (PoP) and were examined. This work studied the false-positive issue inherent in the analysis of neuroimaging data. The results revealed an unprecedented error rate, with 279 of 281 (99.3%) examined references containing at least one error. Nonacademic documents tended to contain more errors than academic publications (U=5117.0; P<.001). This viewpoint article, based on a case study examining GS data accuracy, shows that GS data not only fail to be accurate but also potentially expose researchers, who would use these data without verification, to substantial biases in their analyses and results. Further work must be conducted to assess the consequences of using GS data extracted by PoP.
| S-EPMC9187964 | biostudies-literature
Project description:Rigorous evidence identification is essential for systematic reviews and meta-analyses (evidence syntheses) because the sample selection of relevant studies determines a review's outcome, validity, and explanatory power. Yet, the search systems allowing access to this evidence provide varying levels of precision, recall, and reproducibility and also demand different levels of effort. To date, it remains unclear which search systems are most appropriate for evidence synthesis and why. Advice on which search engines and bibliographic databases to choose for systematic searches is limited and lacking systematic, empirical performance assessments. This study investigates and compares the systematic search qualities of 28 widely used academic search systems, including Google Scholar, PubMed, and Web of Science. A novel, query-based method tests how well users are able to interact and retrieve records with each system. The study is the first to show the extent to which search systems can effectively and efficiently perform (Boolean) searches with regards to precision, recall, and reproducibility. We found substantial differences in the performance of search systems, meaning that their usability in systematic searches varies. Indeed, only half of the search systems analyzed and only a few Open Access databases can be recommended for evidence syntheses without adding substantial caveats. Particularly, our findings demonstrate why Google Scholar is inappropriate as principal search system. We call for database owners to recognize the requirements of evidence synthesis and for academic journals to reassess quality requirements for systematic reviews. Our findings aim to support researchers in conducting better searches for better evidence synthesis.
| S-EPMC7079055 | biostudies-literature
Project description:Using bibliometric data for the evaluation of the research of institutions and individuals is becoming increasingly common. Bibliometric evaluations across disciplines require that the data be normalized to the field because the fields are very different in their citation processes. Generally, the major bibliographic databases such as Web of Science (WoS) and Scopus are used for this but they have the disadvantage of limited coverage in the social science and humanities. Coverage in Google Scholar (GS) is much better but GS has less reliable data and fewer bibliometric tools. This paper tests a method for GS normalization developed by Bornmann et al. (J Assoc Inf Sci Technol 67:2778-2789, 2016) on an alternative set of data involving journal papers, book chapters and conference papers. The results show that GS normalization is possible although at the moment it requires extensive manual involvement in generating and validating the data. A comparison of the normalized results for journal papers with WoS data shows a high degree of convergent validity.
| S-EPMC5502064 | biostudies-other
Project description:To compare voice-activated internet searches by smartphone (two digital assistants) with laptop ones for information and advice related to smoking cessation.Responses to 80 questions on a range of topics related to smoking cessation (including the FAQ from a NHS website), compared for quality.Smartphone and internet searches as performed in New Zealand.Ranked responses to the questions.Google laptop internet searches came first (or first equal) for best quality smoking cessation advice for 83% (66/80) of the responses. Voiced questions to Google Assistant ("OK Google") came first/first equal 76% of the time vs Siri (Apple) at 28%. Google and Google Assistant were statistically significantly better than Siri searches (odds ratio 12.4 and 8.5 respectively, p<0.0001 in each comparison). When asked FAQs from the National Health Service website, or to find information the Centers for Disease Control has made videos on, the best search results used expert sources 59% (31/52) of the time, "some expertise" (eg, Wikipedia) 18% of the time, but also magazines and other low quality sources 19% of the time. Using all three methods failed to find relevant information 8% (6/80) of the time, with Siri having the most failed responses (53% of the time).Google internet searches and Google Assistant were found to be significantly superior to the Siri digital assistant for smoking cessation information. While expert content was returned over half the time, there is still substantial room for improvement in how these software systems deliver smoking cessation advice.
| S-EPMC5874038 | biostudies-literature
Project description: Not available
| S-EPMC4224516 | biostudies-literature