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The difference between highly and poorly cited medical articles in the journal Lancet

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  • Ronald N. Kostoff

    (Office of Naval Research)

Abstract

Characteristics of highly and poorly cited research articles (with Abstracts) published in The Lancet over a three-year period were examined. These characteristics included numerical (numbers of authors, references, citations, Abstract words, journal pages), organizational (first author country, institution type, institution name), and medical (medical condition, study approach, study type, sample size, study outcome). Compared to the least cited articles, the most cited have three to five times the median number of authors per article, fifty to six hundred percent greater median number of references per article, 110 to 490 times the median number of citations per article, 2.5 to almost seven times the median number of Abstract words per article, and 2.5 to 3.5 times the median number of pages per article. The most cited articles’ medical themes emphasize breast cancer, diabetes, coronary circulation, and HIV immune system problems, focusing on large-scale clinical trials of drugs. The least cited articles’ themes essentially do not address the above medical issues, especially from a clinical trials perspective, cover a much broader range of topics, and have much more emphasis on social and reproductive health issues. Finally, for sample sizes of clinical trials specifically, those of the most cited articles ranged from a median of about 1500 to 2500, whereas those of the least cited articles ranged from 30 to 40.

Suggested Citation

  • Ronald N. Kostoff, 2007. "The difference between highly and poorly cited medical articles in the journal Lancet," Scientometrics, Springer;Akadémiai Kiadó, vol. 72(3), pages 513-520, September.
    • Handle: RePEc:spr:scient:v:72:y:2007:i:3:d:10.1007_s11192-007-1573-7
    DOI: 10.1007/s11192-007-1573-7
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    References listed on IDEAS

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    1. Stanley D. Smith, 2004. "Is an Article in a Top Journal a Top Article?," Financial Management, Financial Management Association, vol. 33(4), Winter.
    2. Christian Schloegl & Wolfgang G. Stock, 2004. "Impact and relevance of LIS journals: A scientometric analysis of international and German‐language LIS journals—Citation analysis versus reader survey," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 55(13), pages 1155-1168, November.
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    Cited by:

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    2. Liang, Liming & Zhong, Zhen & Rousseau, Ronald, 2015. "Uncited papers, uncited authors and uncited topics: A case study in library and information science," Journal of Informetrics, Elsevier, vol. 9(1), pages 50-58.
    3. André Andrian Padial & João Carlos Nabout & Tadeu Siqueira & Luis Mauricio Bini & José Alexandre Felizola Diniz-Filho, 2010. "Weak evidence for determinants of citation frequency in ecological articles," Scientometrics, Springer;Akadémiai Kiadó, vol. 85(1), pages 1-12, October.
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    6. Belkhouja, Mustapha & Yoon, Hyungseok (David), 2018. "How does openness influence the impact of a scholar’s research? An analysis of business scholars’ citations over their careers," Research Policy, Elsevier, vol. 47(10), pages 2037-2047.
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    10. Mike Thelwall, 2016. "Interpreting correlations between citation counts and other indicators," Scientometrics, Springer;Akadémiai Kiadó, vol. 108(1), pages 337-347, July.
    11. Uddin, Shahadat & Khan, Arif, 2016. "The impact of author-selected keywords on citation counts," Journal of Informetrics, Elsevier, vol. 10(4), pages 1166-1177.
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    13. Thomas R. Anderson & Robin K. S. Hankin & Peter D. Killworth, 2008. "Beyond the Durfee square: Enhancing the h-index to score total publication output," Scientometrics, Springer;Akadémiai Kiadó, vol. 76(3), pages 577-588, September.
    14. Ehsan Mohammadi & Mike Thelwall, 2013. "Assessing non-standard article impact using F1000 labels," Scientometrics, Springer;Akadémiai Kiadó, vol. 97(2), pages 383-395, November.
    15. Juan Xie & Kaile Gong & Ying Cheng & Qing Ke, 2019. "The correlation between paper length and citations: a meta-analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 118(3), pages 763-786, March.
    16. Li, Xin & Wen, Yang & Jiang, Jiaojiao & Daim, Tugrul & Huang, Lucheng, 2022. "Identifying potential breakthrough research: A machine learning method using scientific papers and Twitter data," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
    17. John N. Parker & Stefano Allesina & Christopher J. Lortie, 2013. "Characterizing a scientific elite (B): publication and citation patterns of the most highly cited scientists in environmental science and ecology," Scientometrics, Springer;Akadémiai Kiadó, vol. 94(2), pages 469-480, February.
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