Comparison of PubMed and Google Scholar Literature Searches

By Michael E. Anders, D. Evans. Respiratory care, 2010

1,057 citations recorded by the source corpus.

BACKGROUND: Literature searches are essential to evidence-based respiratory care. To conduct literature searches, respiratory therapists rely on search engines to retrieve information, but there is a dearth of literature on the comparative efficiencies of search engines for researching clinical questions in respiratory care. OBJECTIVE: To compare PubMed and Google Scholar search results for clinical topics in respiratory care to that of a benchmark. METHODS: We performed literature searches with PubMed and Google Scholar, on 3 clinical topics. In PubMed we used the Clinical Queries search filter. In Google Scholar we used the search filters in the Advanced Scholar Search option. We used the reference list of a related Cochrane Collaboration evidence-based systematic review as the benchmark for each of the search results. We calculated recall (sensitivity) and precision (positive predictive value) with 2 × 2 contingency tables. We compared the results with the chi-square test of independence and Fisher's exact test. RESULTS: PubMed and Google Scholar had similar recall for both overall search results (71% vs 69%) and full-text results (43% vs 51%). PubMed had better precision than Google Scholar for both overall search results (13% vs 0.07%, P < .001) and full-text results (8% vs 0.05%, P < .001). CONCLUSIONS: Our results suggest that PubMed searches with the Clinical Queries filter are more precise than with the Advanced Scholar Search in Google Scholar for respiratory care topics. PubMed appears to be more practical to conduct efficient, valid searches for informing evidence-based patient-care protocols, for guiding the care of individual patients, and for educational purposes.

Subfields: General Health Professions, Sociology and Political Science, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/205a88f356decff04982842250be5055e581c90b

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Citations in the corpus

Is the coverage of google scholar enough to be used alone for systematic reviews (2013, background): “GS has been reported to be less precise than PubMed, since it retrieves hundreds or thousands of documents, most of them being irrelevant [29,30].”
Expanding the MicroRNA Targeting Code: Functional Sites with Centered Pairing (2010, methodology): “Uncapped 5’ ends of GSTM3 mRNA degradation products were identified using the 5’-RACE kit (Invitrogen), as in (Jones-Rhoades and Bartel, 2004), starting with cells in which XRN-1 mRNA was knocked-down more than 90%, as confirmed by RT-PCR (Aleman et al., 2007).”
The Role of Google Scholar in Evidence Reviews and Its Applicability to Grey Literature Searching (2015, background): “Our results show that Google Scholar is indeed a useful platform for searching for environmental science grey literature that would benefit researchers such as systematic reviewers, agreeing with previous research in medicine [32,33].”
Machine learning and deep learning—A review for ecologists (2022, background): “DL can identify species in different data types (Ferreira et al., 2020; Gray et al., 2019; Guirado et al., 2018 ; Mäder et al., 2021 ; Tabak et al., 2019; Torney et al., 2019 ; Willi et al., 2019 ) or extract information such as traits from raw data (Dunker et al., 2020 ).”
A smartphone app reveals erratic diurnal eating patterns in humans that can be modulated for health benefits (2015, methodology): “To compare the eating pattern with diurnal activity period, wrist actigraphy (Blood et al., 1997) data were collected from 47 randomly selected participants using a CamNTech Motionwatch 8, a device that measured both activity and light.”