The Hidden Structure of Neuropsychology: Text Mining of the Journal Cortex: 1991-2001

Kostoff, Ronald N.; Buchtel, Henry A.; Andrews, John; Pfeil, Kirstin M

doi:10.1016/s0010-9452(08)70885-2

Cited by 12 publications

(5 citation statements)

References 28 publications

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“…(2) Biopsy (2) Epidemiol (4) Lab tests (2) 1997 summary -least cited Radiology (4) Overview (5) Overview (6) Large (4) Favorable (5) Lab tests (3) Assessments (2) Median (755) Analysis (2) Epidemiology (2) Small (2) Survey (2) Median (7) 1998 summary -most cited Breast cancer (5) Drugs (11) Clinical trial(12) Large(16) Favorable (8) Diabetes (2) Blood assays (2) Clinical assays(2) (median 3445) Moderately favor (3) Myocardial infarct (2) Partially favor (2) 1998 summary -least cited Social health(4) Evaluation(4) Assessment(4) Small (6) N.a. (11) Stroke (3) Treatment protocol (3) Propose reform(4) (median 28) Favorable (4) Diagnostic tests (2) Clinical trial (3) Unfavorable (1) Clinical diagnostics (2) Inconclusive (1) 1999 summary -most cited Cardiovascular(6) Drugs(9) Clin trial(14) Large(15) Favorable (9) HIV-1 (5) Assessment (3) Median(1000) Inconclusive (4) Hypertension (2) Moderately fav (3) 1999 summary -least cited Lab test (6) Case study (4) Small (7) Favorable (9) Data analysis (4) Lab test (4) Large (3) Moderately favorable (2) Evaluation (2) Assessment (4) Survey (2) Survey (2) For all three years, there are substantial differences between the most and least cited articles in both the bibliometrics and medical/technical issues. Compared to the least cited articles, the most cited article bibliometrics reflect, for the three years, approximately:…”

Section: Resultsmentioning

confidence: 99%

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

Kostoff

2007

Scientometrics

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Characteristics of highly and poorly cited research articles (with Abstracts) published in TheLancet 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.

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Section: Resultsmentioning

confidence: 99%

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

Kostoff

2007

Scientometrics

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“…Text mining is a very useful methodology to develop a query for comprehensive information retrieval from published literature [3,4]. It is also an important and fascinating area of modern analytics with its extremely iterative process [5].…”

Section: Literaturementioning

confidence: 99%

Trend Topic Analysis for Wind Energy Researches: A Data Mining Approach Using Text Mining

Eroğlu

Seçkiner

2016

J. Technol. Innov. Renew. Energy

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This study reviews and analyses the recent research and development and trends in the applications of wind energy and it also discusses and summarizes the topic. We show the usage and the influence of text mining on the different aspects of wind energy systems especially for hot topics and trends of wind energy area. Text mining provides the state of the art in this area that will be a good guidance for future research work. The main results achieved from the study have shown that the text mining technique are adequate for serving as a proof of concept and as a test-bed for deriving requirements for the development of more generally applicable text mining tools and services within wind energy science.

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“…A scientometric analysis of articles published in the journal Cortex (a neuropsychology journal) was performed ). [8] One highlight was the comparison of citation performance of Cortex with two similar competitive journals: Brain and Neuropsychologia. The following experiment was run.…”

Section: Citation Analysis -Comparison Of Three Neuropsychology Journalsmentioning

confidence: 99%

Text mining for science and technology: A review - Part II-citation and discovery

Kostoff¹

2013

J Sci Res

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This is the second part of a two-part review that addresses three major text mining subdivisions: Characterization; seminal literature review (SLR); literature-related discovery and innovation (LRDI). Part I, published in the inaugural issue of The Journal of Scientometric Research (Kostoff 2012a), [1] focuses on characterization, mainly its non-citation components. Part II, published in this second issue of the Journal of Scientometric Research, focuses on the citation component of characterization, the citation-based SLR, and the citation-enabled LRDI.

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The Hidden Structure of Neuropsychology: Text Mining of the Journal Cortex: 1991-2001

Cited by 12 publications

References 28 publications

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

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

Trend Topic Analysis for Wind Energy Researches: A Data Mining Approach Using Text Mining

Text mining for science and technology: A review - Part II-citation and discovery

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