Google-Reflected Information on Drugs Used for Anesthesia: Time Course of Growth in Number of Web Pages

Main Article Content

Kamen V. Vlassakov Igor Kissin

Abstract

Purpose: The aim of this study was to determine the time course of growth in general Google-reflected information on drugs used for anesthesia. As a contrast to the changes in general Google-reflected information we used the changes in academic PubMed-reflected information.


Methods: General Google-reflected information on anesthetics was assessed by counting the number of Google Web pages. Academic information was assessed by counting the number of articles in medico-biological journals covered by the PubMed database (The National Library of Medicine). The ratio of Google Web pages to PubMed articles (G/P Ratio) was used to indicate prevalence of Google-related information. Twenty-five agents used for anesthesia were selected from three pharmacological groups – general anesthetics, local anesthetics, and opioids -- based on the frequency of their association with anesthesia in academic medical journals. The time course of growth in general Google-reflected information was determined for five 5-year periods, from 1993 to 2017.


Results: With the growing role of the Web, the number of Google Web pages on drugs used for anesthesia increased rapidly. As a result, the relationship between general Google-reflected and academic PubMed-reflected information on anesthetics profoundly changed. Before the 1998-2002 period, the number of Google Web pages on anesthetics was mostly a fraction of the number of PubMed articles. By the 2013-2017 period, the relationship was completely reversed: for any anesthetic, the number of Google Web pages was at least three times greater than the number of PubMed articles. However, the relationship of general Web-related information and academic information with different anesthetics was very variable. In 2013-2017, the G/P Ratio, indicating the magnitude of general information dominance, for the 25 agents varied from 3.0 (remifentanil) to 23.2 (oxycodone). The dominance of Google information was especially profound with drugs that have a wider spectrum of possible use beyond the field of anesthesia, such as oxycodone or diazepam.


Conclusion: General Google-reflected information is rapidly growing and, as a result, its dominance over academic PubMed-reflected information is constantly increasing.


 

Keywords: Bibliometrics, General anesthetics, Google Web pages, Local anesthetics, Opioids, Scientometrics, World Wide Web

Article Details

How to Cite
VLASSAKOV, Kamen V.; KISSIN, Igor. Google-Reflected Information on Drugs Used for Anesthesia: Time Course of Growth in Number of Web Pages. Medical Research Archives, [S.l.], v. 10, n. 2, feb. 2022. ISSN 2375-1924. Available at: <https://esmed.org/MRA/mra/article/view/2688>. Date accessed: 04 dec. 2024. doi: https://doi.org/10.18103/mra.v10i2.2688.
Section
Research Articles

References

1. Vlassakov KV. Kissin I. Changes in publication-based academic interest in local anesthetics over the past 50 years. J Anesth Hist. 2016;2:73-78
2. Correll DJ, Vlassakov KV, Kissin I. Recent history of publication-based academic interest in general anesthetics. J Anesth Hist. 2018;4:109-114
3. Vlassakov KV, Kissin I. Scientometrics of anesthetic drugs and their techniques of administration, 1984-2014. Drugs Des Devel Ther. 2014;8:2463-2473
4. Thelwall M, Vaughan L, Bjorneborn L. Webometrics. Annu Rev Inform Sci. 2005;39:81-135
5. Silberg WM, Lundberg GD, Musacchio RA. Assessing, controlling, and assuring the quality of medical information on the internet. Caveant lector et viewor – let the reader and buyer beware. JAMA 1997;277:1244-1245
6. Griffiths KM, Christensen H. Quality of web based information on treatment of depression: cross sectional survey. BMJ 2000;321:1511-1515
7. Kunst H, Groot D, Latthe PM, Latthe M, Khan KS. Accuracy of information on apparently credible websites: survey of five common health topics. BMJ 2002;324:581-582
8. Abbasi A, Zhang Z, Zimbra D, Chen H, Nunamaker JF, Jr. Detecting fake websites: The contribution of statistical learning theory. Mis Quarterly 2010; 34:435-461
9. Nichols T. The Death of Expertise: The campaign against established knowledge and why it matters. Oxford University Press, 1st ed. 2017