Skip to main content
  • Research article
  • Open access
  • Published:

Is the coverage of google scholar enough to be used alone for systematic reviews



In searches for clinical trials and systematic reviews, it is said that Google Scholar (GS) should never be used in isolation, but in addition to PubMed, Cochrane, and other trusted sources of information. We therefore performed a study to assess the coverage of GS specifically for the studies included in systematic reviews and evaluate if GS was sensitive enough to be used alone for systematic reviews.


All the original studies included in 29 systematic reviews published in the Cochrane Database Syst Rev or in the JAMA in 2009 were gathered in a gold standard database. GS was searched for all these studies one by one to assess the percentage of studies which could have been identified by searching only GS.


All the 738 original studies included in the gold standard database were retrieved in GS (100%).


The coverage of GS for the studies included in the systematic reviews is 100%. If the authors of the 29 systematic reviews had used only GS, no reference would have been missed. With some improvement in the research options, to increase its precision, GS could become the leading bibliographic database in medicine and could be used alone for systematic reviews.

Peer Review reports


The release of the beta version of Google Scholar (GS) ( in November 2004 generated much media coverage and academic commentary. It has been met with both enthusiasm and criticism but Google and GS now lead more visitors to many biomedical journal websites than does Medline via its PubMed interface [13].

GS searches retrieve results that include scholarly literature citations as well as peer-reviewed publications, theses, books, abstracts, and other articles from academic publishers, professional organizations, and preprint repositories, universities, and other scholarly organizations. Therefore, GS is able to retrieve more types of literature compared with medical literature database retrieval search engines, like PubMed [4]. GS is also able to identify some of the references of PubMed, but not all [5].

Doctors are encouraged to consult GS for browsing and serendipitous discovery, not for literature reviews [1]. In searches for clinical trials and systematic reviews, it is said that GS should never be used in isolation, but in addition to PubMed, Cochrane, and other trusted sources of information [1]. Many studies have demonstrated that a single search engine does not capture all of the available articles, and using two or more databases provides greater coverage of all possible citations [617].

Nevertheless, the coverage of GS is increasing and, despite the fact that it is said to be not exhaustive, is it exhaustive enough for the studies that are considered of enough quality or relevance for systematic reviews [18].

Therefore, the objective of this study was to assess the coverage of GS, and its potential recall, specifically for such studies, and therefore to assess if this database could be used alone for systematic reviews.


The first step aimed at identifying a subset of studies selected by experts to be included in systematic reviews. We searched Medline in December 2009 for the systematic reviews published in the JAMA or the Cochrane Library. For the JAMA, we used the most specific search string proposed by Montori et al., with limits for the years 2008 and 2009 [19]. For the Cochrane Library, we examined all the systematic reviews published in the Cochrane Database Syst Rev. 2009 Jul 8;(3).

We excluded the systematic reviews using less than 2 bibliographic databases in their search and those which restricted the search to English language studies.

The gold standard database was then built by gathering all the studies included in the systematic reviews we selected, excluding abstracts and personal communications. We considered Gray literature (i.e. written material that is not published commercially or is not generally accessible) as a specific subset, but we included these references in the gold standard database.

GS was searched for each reference, one by one, by searching with the title of each of the studies included in the gold standard database. Recall (i.e. the proportion of studies retrieved from the database) of GS were computed for each review published in the Cochrane Library or the JAMA.


Overall, 14 reviews from the Cochrane library and 15 reviews from the JAMA were included. To identify all the possible relevant studies, each systematic review from the Cochrane Library and from the JAMA had searched between 3 and 10 (mean: 5.4) and between 2 and 9 (mean : 4) different databases, respectively. All of them searched Medline and 17 mentioned to have also scanned the reference list of the studies they included.

The 29 systematic reviews had included 755 original studies. Among them, 733 were published in peer-reviewed journals and 5 were detailed only in document belonging to the gray literature. The 18 remaining studies were referenced only as an abstract or as personal communication and were therefore not included in the gold standard database, which included finally 738 original studies. All the 738 studies were identified in GS, leading to 100% coverage.

The detailed results are presented in Table 1.

Table 1 Recall of Google scholar for the 29 systematic reviews

As a side result, we discovered that a striking number of bibliographic references included major errors, i.e. errors that involve the data elements by which references are searched by users in Medline [20]. Overall, 10 references contained at least one major error, some of them containing up to 3 major errors.

Some of the reviews concentrated these citation errors. For example, among the 24 references included in the Cochrane review " The effects of antimicrobial therapy on bacterial vaginosis in non-pregnant women", 5 contained at least one major error.


Performing systematic reviews is a complex and time consuming task, because of the body of literature to be searched and the high number of databases that must be used, considering that no one of them is considered exhaustive. The use of GS is increasing, as well as its coverage, and we wanted to assess if this coverage is high enough to be used alone in systematic reviews.

GS allowed to retrieve 100% of the studies included in the systematic reviews we studied, and which covered many different fields of medicine.

Although GS does not cover all the medical literature, we therefore observed that its coverage of the studies of sufficient quality or relevance to be included in a systematic review was complete. In other words, if the authors of these 29 systematic reviews had used only GS, they would have obtained the very same results.

The validity of our gold standard database could nevertheless be questioned. To identify the studies that worth to be included in a systematic review, we relied on the works of the experts used as reviewer in the systematic reviews we included, since all of them used at least 2 independent reviewers. Furthermore, we excluded from our gold standard database personal communications, because they cannot be retrieved by any database, and abstracts because it has been clearly demonstrated that such abstracts often display non-valid results [21, 22]. Considering the methods used by the authors of the systematic reviews we selected, the use of at least two independent reviewers to select relevant articles in these reviews, the high number of databases searched and the absence of restriction to English studies in each of them, we can also assume that, for each topic covered, all the relevant studies were identified. Therefore, we can assume that our gold-standard database really included all the studies of sufficient quality and relevant to the topics covered by the systematic reviews, and only them.

We chose to study the systematic reviews published by the JAMA and Cochrane because they usually don’t restrict their search to English literature and they use more than one database to perform the search, which is not the case of most of the systematic reviews published by the Annals of Internal Medicine, for example.

Although the recall of GS was 100%, the amount of information delivered by GS was heterogeneous. Yet, some of the studies were only identified as "citations", which means that GS only displayed the authors, the title of the article and the name, year and pages of the journals. This can be considered as insufficient, but traditional biomedical databases (such as Medline or Embase) do the same for old articles or for articles published in another language that English. Furthermore, this is exactly the same situation when authors of systematic reviews perform hand searching in the reference list of selected articles. Therefore, we considered valid to include these hits as positive results.

This 100% coverage of GS can be seen as amazing, since no single database is supposed to be exhaustive, even for good quality studies. For example, the recall ratios of Medline for randomized control trials (RCTs) only stand between 35% and 56% [23, 24]. Since GS accesses only 1 million of the some 15 million records at PubMed, how can our results be explained? In fact, through agreements with publishers, GS accesses the “invisible” or “deep” Web, that is, commercial Web sites the automated “spiders” used by search engines such as Google cannot access. Furthermore, we observed in our study that most of the articles indentified by GS were found directly on the publishing journal web-sites, and not on the PubMed web-site.

Nevertheless, while its advantages are substantial, GS is not without flaws. The shortcomings of the system and its search interface have been well documented in the literature and include lack of reliable advanced search functions (e.g. no MeSH term subheading search function), lack of controlled vocabulary, lack of a “similar pages” feature, and issues regarding scope of coverage and currency [4, 5, 25]. Furthermore, whereas PubMed displays results in a chronological order, GS places more relevance on articles that are cited most often. Therefore, the citations located are reportedly biased toward older literature [26, 27]. This last point can also be viewed as an advantage, since it allows to identify quickly landmark articles, i.e. articles of importance in a field. Yet, when comparing searches with PubMed and Google Scholar by evaluating the first 20 articles recovered for four clinical questions for relevance and quality, Nourbakhsh and coll. demonstrated that GS provided more relevant results that PubMed, although the difference was not significant (p=0.116) [28].

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]. Nevertheless, we should not overestimate the precision of PubMed in real life since Precision and recall of a search in a database is highly dependent on the skills of the user [10]. Many of them overestimate the quality of their searching performance, and experienced reference librarians typically retrieve about twice as many citations as do less experienced users [31, 32].

Although this was not the purpose of our study, we tried to assess the precision of GS for some of the clinical questions that were studied by the systematic reviews.

For example, searching for "(Erythropoietin or Darbepoetin) cancer" in GS gave a recall of 100% and a precision of 0.1% (36,630 articles found, for 36 included in the systematic review). In GS, the search string "(depression treatment placebo antidepressant) ("general practice" OR "Primary care")" identified 16100 articles, leading to a recall of 100% and a precision of 0.09 (14 articles included in the corresponding systematic review).


In conclusion, the coverage of GS is much higher than previously thought for high quality studies. GS is highly sensitive, easy to search and could be the first choice for systematic reviews or meta-analysis. It could even be used alone. It just requires some improvement in the advanced search features to improve its precision and to become the leading bibliographic database in medicine.


  1. Giustini D: How Google is changing medicine. BMJ. 2005, 331: 1487-1488. 10.1136/bmj.331.7531.1487.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Lindberg DA: Searching the medical literature. NEJM. 2006, 354: 2393-

    CAS  PubMed  Google Scholar 

  3. Wang Y, Howard P: Google Scholar Usage: An Academic Library's Experience. J Web Librarianship. 2012, 6 (2): 94-108. 10.1080/19322909.2012.672067.

    Article  CAS  Google Scholar 

  4. Freeman MK, Lauderdale SA, Kendrach MG, Woolley TW: Google Scholar versus PubMed in locating primary literature to answer drug-related questions. Ann Pharmacother. 2009, 43: 478-484. 10.1345/aph.1L223.

    Article  PubMed  Google Scholar 

  5. Shultz M: Comparing test searches in PubMed and Google Scholar. J Med Libr Assoc. 2007, 95: 442-445. 10.3163/1536-5050.95.4.442.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Wilkins T, Gillies RA, Davies K: EMBASE versus MEDLINE for family medicine searches: can MEDLINE searches find the forest or a tree?. Can Fam Physician. 2005, 51: 849-

    PubMed Central  Google Scholar 

  7. Verbeek J, Salmi J, Pasternack I, Jauhiainen M, Laamanen I, Schaafsma F, Hulshof C, van Dijk F: A search strategy for occupational health intervention studies. Occup Environ Med. 2005, 62: 682-687. 10.1136/oem.2004.019117.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Minozzi S, Pistotti V, Forni M: Searching for rehabilitation articles on MEDLINE and EMBASE. An example with cross-over design. Arch Phys Med Rehabil. 2000, 81: 720-722.

    Article  CAS  PubMed  Google Scholar 

  9. McDonald S, Taylor L, Adams C: Searching the right database. A comparison of four databases for psychiatry journals. Health Libr Rev. 1999, 16: 151-156. 10.1046/j.1365-2532.1999.00222.x.

    Article  CAS  PubMed  Google Scholar 

  10. Watson RJ, Richardson PH: Identifying randomized controlled trials of cognitive therapy for depression: comparing the efficiency of Embase, Medline and PsycINFO bibliographic databases. Br J Med Psychol. 1999, 72: 535-542. 10.1348/000711299160220.

    Article  PubMed  Google Scholar 

  11. Farriol M, Jordà-Olives M, Padró JB: Bibliographic information retrieval in the field of artificial nutrition. Clin Nutr. 1998, 17: 217-222. 10.1016/S0261-5614(98)80062-9.

    Article  CAS  PubMed  Google Scholar 

  12. Gehanno JF, Paris C, Thirion B, Caillard JF: Assessment of bibliographic databases performance in information retrieval for occupational and environmental toxicology. Occup Environ Med. 1998, 55: 562-566. 10.1136/oem.55.8.562.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Woods D, Trewheellar K: Medline and Embase complement each other in literature searches. BMJ. 1998, 316: 1166-

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Barillot MJ, Sarrut B, Doreau CG: Evaluation of drug interaction document citation in nine on-line bibliographic databases. Ann Pharmacother. 1997, 31: 45-49.

    CAS  PubMed  Google Scholar 

  15. Brazier H, Begley CM: Selecting a database for literature searches in nursing: MEDLINE or CINAHL?. J Adv Nurs. 1996, 24: 868-875. 10.1046/j.1365-2648.1996.26426.x.

    Article  CAS  PubMed  Google Scholar 

  16. Burnham J, Shearer B: Comparison of CINAHL, EMBASE, and MEDLINE databases for the nurse researcher. Med Ref Serv Q. 1993, 12: 45-57.

    Article  CAS  PubMed  Google Scholar 

  17. Gallagher KE, Hulbert LA, Sullivan CP: Full-text and bibliographic database searching in the health sciences: an exploratory study comparing CCML and MEDLINE. Med Ref Serv Q. 1990, 9: 17-25.

    Article  CAS  PubMed  Google Scholar 

  18. Beckmann M, von Wehrden H: Where you search is what you get: literature mining – Google Scholar versus Web of Science using a data set from a literature search in vegetation science. J Veg Sci. 2012, 23 (6): 1197-1199. 10.1111/j.1654-1103.2012.01454.x.

    Article  Google Scholar 

  19. Montori VM, Wilczynski NL, Morgan D, Haynes RB: Optimal search strategies for retrieving systematic reviews from Medline: analytical survey. BMJ. 2005, 330: 68-10.1136/bmj.38336.804167.47.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Gehanno JF, Darmoni SJ, Caillard JF: Major inaccuracies in articles citing occupational or environmental medicine papers and their implications. J Med Libr Assoc. 2005, 93: 118-121.

    PubMed  PubMed Central  Google Scholar 

  21. Scherer RW, Langenberg P, Von Elm E: Full publication of results initially presented in abstracts. Cochrane Database Syst Rev. 2007, 2: MR000005-

    PubMed  Google Scholar 

  22. Rollin L, Darmoni S, Caillard J, Gehanno J: Fate of abstracts presented at an International Commission on Occupational Health (ICOH) congress - followed by publication in peer-reviewed journals?. Scand J Work Environ Health. 2009, 35: 461-465. 10.5271/sjweh.1362.

    Article  PubMed  Google Scholar 

  23. Türp JC, Schulte J, Antes G: Nearly half of dental randomized controlled trials published in German are not included in Medline. Eur J Oral Sci. 2002, 110: 405-411. 10.1034/j.1600-0722.2002.21343.x.

    Article  PubMed  Google Scholar 

  24. Hopewell S, Clarke M, Lusher A, Lefebvre C, Westby M: A comparison of hand searching versus MEDLINE searching to identify reports of randomized controlled trials. Stat Med. 2002, 21: 1625-1634. 10.1002/sim.1191.

    Article  CAS  PubMed  Google Scholar 

  25. Aguillo IF: Is Google Scholar useful for bibliometrics? A webometric analysis. Scientometrics. 2012, 91: 343-351. 10.1007/s11192-011-0582-8.

    Article  CAS  Google Scholar 

  26. Henderson J: Google Scholar: a source for clinicians?. CMAJ. 2005, 172: 1549-1550.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Vine R: Google Scholar. J Med Libr Assoc. 2006, 94: 97-99.

    PubMed Central  Google Scholar 

  28. Nourbakhsh E, Nugent R, Wang H, Cevik C, Nugent K: Medical literature searches: a comparison of PubMed and Google Scholar. Health Info Libr J. 2012, 29 (3): 214-222. 10.1111/j.1471-1842.2012.00992.x.

    Article  PubMed  Google Scholar 

  29. Anders ME, Evans DP: Comparison of PubMed and Google Scholar literature searches. Respir Care. 2012, 55: 578-583.

    Google Scholar 

  30. Mastrangelo G, Fadda E, Rossi CR, Zamprogno E, Buja A, Cegolon L: Literature search on risk factors for sarcoma: PubMed and Google Scholar may be complementary sources. BMC Res Notes. 2010, 3: 131-10.1186/1756-0500-3-131.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Hersh WR, Hickam DH: How well do physicians use electronic information retrieval systems? A framework for investigation and systematic review. JAMA. 1998, 280: 1347-1352. 10.1001/jama.280.15.1347.

    Article  CAS  PubMed  Google Scholar 

  32. Haynes RB, McKibbon KA, Walker CJ, Ryan N, Fitzgerald D, Ramsden MF: Online access to MEDLINE in clinical settings. A study of use and usefulness. Ann Intern Med. 1990, 112: 78-84.

    Article  CAS  PubMed  Google Scholar 

Pre-publication history

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to Jean-François Gehanno.

Additional information

Competing interests

The authors declare they have no competing interest.

Authors’ contribution

JFG conceived of the study. JFG and LR collected the data. JFG, LR and SJD analyzed the data and drafted the manuscript. All authors read and approved the final manuscript.

Rights and permissions

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Reprints and permissions

About this article

Cite this article

Gehanno, JF., Rollin, L. & Darmoni, S. Is the coverage of google scholar enough to be used alone for systematic reviews. BMC Med Inform Decis Mak 13, 7 (2013).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: