Warning: include_once(../article_type.php): failed to open stream: No such file or directory in /home/suxhorbncfos/public_html/gjo/GJO.MS.ID.555986.php on line 216
Warning: include_once(): Failed opening '../article_type.php' for inclusion (include_path='.:/opt/alt/php56/usr/share/pear:/opt/alt/php56/usr/share/php') in /home/suxhorbncfos/public_html/gjo/GJO.MS.ID.555986.php on line 216
The Most Cited Author Who Published Papers in the Journal of Otolaryngology: A Bibliometric Study
Tsair Wei Chien1, Hsien-Yi Wang2 and Willy Chou3,4*
1Medical Research Department, Taiwan
2Department of Sport Management, Taiwan
3 Department of Physical Medicine and Rehabilitation, Taiwan
4 Department of Recreation, Health-Care Management & Institute of recreation, Taiwan
Submission: November 16, 2018; Published: November 27, 2018
*Corresponding author: Willy Chou, Chi-Mei Medical Center, 901 Chung Hwa Road, Yung Kung Dist, Tainan 710, Taiwan.
How to cite this article: Tsair Wei Chien, Hsien-Yi Wang, Willy Chou. The Most Cited Author Who Published Papers in the Journal of Otolaryngology: A
Bibliometric Study. Glob J Oto, 2018; 18(3): 555986. DOI: 10.19080/GJO.2018.18.555986
Background: Individual researchers’ achievements(IRA) were determined by the number of publications and citations using bibliometric indices(e.g., author impact factor(AIF)) which were criticized without considering authorship weighted contributions. The objective of this study is to develop a scheme for quantifying author contributions which can be applied to calculate the author’s IRA. Which article topics with higher impact factor(IF) are also investigated
Methods: We obtained abstracts from Medline by searching the keywords of “Otolaryngology”[Journal]). A total of 291 articles were retrieved in 1978 and cited 94 times by published papers in Pubmed Central. An authorship-weighted scheme (AWS) was used for quantifying coauthor contributions. The number of citations on article topics was analyzed using bibliometric indices(x-index, author impact factor(AIF), L=weithted citations and Ag=mean on core articles for g-index). We plotted the clusters, including (i) the top 10 author clusters which collaborated most in centrality degree of social network analysis(SNA); (ii) most-cited authors, (ii) article types classified by SNA and major medical subject headings(MeSH) dispersed on a dashboard, and (iii) one way ANOVA applied to analyze the difference among clusters of author collaborations and Mesh terms. Visual dashboards were shown on Google Maps.
Results: This study found that (i) the most cited authors is P A Santi(PMID=113741 cited 10 times) with high AIF=10; (ii) the top three topics are physiology, surgery, and pathology; (iii) the most number of cited article is entitled by “Arteriolar sclerosis as a cause of presbycusis” with PMID=113738. Differences in impact factor were found among MeSH clusters with statistics of F(9,37)=2.287 and p=0.025.
Conclusions: The AWS-based x-index can be applied to other academic fields for understanding the most highly cited authors in a discipline or on an academic topic.
Keywords: Pubmed center; Authorship-Weighted Scheme; Social Network Analysis; Google MAPS; x-index
Otorhinolaryngology (also called otolaryngology and otolaryngology-head and neck surgery) is a surgical subspecialty within medicine that deals with conditions of the ear, nose, and throat (ENT) and related structures of the head and neck . Doctors who specialize in this field are called otorhinolaryngologists, otolaryngologists, ENT doctors, ENT surgeons, or head and neck surgeons. Patients often seek treatment from an otorhinolaryngologists for diseases of the ear, nose, throat, base of the skull, and for the surgical management of cancers and benign tumors of the head and neck . As of Novenber10, 2018 154899 abstracts were found by searching the keyword of Otorhinolaryngology in Pubmed Central(PMC), and
2705 in article title only. There are four topics that intrigue us to study, including
a. which terms of author collaborations are most outstanding in the academic field related to otorhinolaryngology?
b. which research teams and article types were highly cited by published papers?
c. which authors whose papers were cited most in otorhinolaryngology?
d. Is any difference among research teams or article types regarding the topic of otorhinolaryngology?
It is hard to find the relationship between multiple entities.
The social network analysis(SNA) has been applied to investigate
the correlations of entities in a network by the concept of cooccurrence
[2-4]. Many data scientists have developed ways to
discover new knowledge from the vast quantities of increasingly
available information , particularly applying social network
analysis (SNA) [6-8] to author collaborations in academic fields.
Authorship collaboration using SNA has been investigated by
many authors in recent years . because co-authors among
researchers form a type of social network. Whether the keyword
network in otorhinolaryngology earns different impact factors is
interesting to explore.
We are thus interested in using SNA to explore the features
in otorhinolaryngology from published papers we observed
in Medline library. However, the authorship weighted scheme
should be applied to fairly report the most cited authors in a
discipline[2,3]. Google maps have provided users to gain an overall
geospatial visualization [9,10]. Few were found using Google
Maps to show the study results when searching the keyword
google map [Title]. Even many papers [6-8]. have investigated
co-author collaboration in the literature. However, none display
these results using SNA and dashboards on Google Maps. Our aims
are to present (i) the top 10 author clusters which collaborated
most in centrality degree of social network analysis(SNA); (ii)
most-cited authors, (ii) article types classified by SNA and major
medical subject headings(MeSH) dispersed on a ashboard, and
(iii) one way ANOVA to analyze the difference among clusters of
author collaborations and Mesh clusters.
We obtained 291 abstracts from Medline by searching the
keywords of “Otolaryngology”[Journal]). A total number of 94
citing articles were successfully matched to the 37 cited papers in
An authorship-weighted scheme (AWS) was based on the
Rasch rating scale model  for quantifying author contributions
and letting the sum equals 1, see Equation (1) and (2) [2,3]:
As a result, more importance is given to the first (=exp(m),
primary) and the last (=exp(m-1), while it is assumed that the
others (the middle authors) have made smaller contributions
. In Eq.2, the smallest portion(=exp(0)=1) is assigned to the
second last author with the odds=1 as the basic reference [2,3].
The AIF of an author A can be defined in Eq.3:), (3)
A total number of 291 authors were collected for calculating
their metrics and AIFs based on citable papers in PMD in 2978
only. All metrics and AIFs were located on dashboards using SNA
and Google Maps to display.
In keeping with the Pajek guidelines  using SNA, we
defined an author as a node(or an actor) that is connected to
another counterpart at another node through the edge of a line.
Usually, another weight is defined by the number of connections
between two nodes [2,3]. Three main centrality measures (i.e.,
degree, closeness, and betweenness) are frequently used to
evaluate the influence (or power) momentum of an entity (e.g.,
the author or keyword) in a network [14,15]. Centrality is an
important index to analyze the network. Any individual authors
lie in the center of the social network will determine its influence
on the network and its speed to gain information . In this
study, the degree centrality was applied to explore the keywords
and author collaborations.
SNA was applied to classify the major medical subject
headings(MeSH) into articles on the topic of otolaryngology. The
algorithm of community partition was performed to identify and
separate the clusters.
Each article was, in turn, assigned to a specific MeSH cluster
through the maximum likelihood estimation. As such, each article
was classified as one of the MeSH clusters. Each MeSH cluster
can be characterized by bibliometric indices which internal
consistency (IC) can be examined by Kendall’s coefficient of
concordance (W)  across keyword clusters. If the agreement
is accepted by the statistical alpha level (<0.05) .
The centrality measures are computed by SNA algorithm in
Pajek. We imported them into an author-made Excel module and
then created a page of Hyper Text Mark-up Language(HTML) used
for Google Maps. Bibliometric indices regarding h-index, the
author impact factor (AIF)[21, 22], and others(i.e., g-index,
Ag, x-index, and L-index ). The L-index is the root of
the total citations for authors used in this study.
The most cited authors is PA Santi(PMID=113741
cited 10 times) with high AIF=10  until 2018 with high metrics(citable=0.73, cited=7.31, AIF=10, Ag=7.31, h=1.31, =1,
x=2.7). Interested readers are invited to scan the QR-Code in
Figure 1 to examine the author’s publication outputs in PMC by
clicking the specific author bobble.
The top 10 MeSH clusters were separated as shown in Figure
2. The representative terms with the most degree centrality are
shown for each cluster. The interested readers are recommended
to scan the QR-coed in Figure 2 to see the detailed information
in PMC by clicking the word of publication when the specific
keyword bubble is selected. The top three topics are physiology,
surgery, and pathology.
Table 1 at the top shows the counts of citable, cited articles
and metrics across the MeSH clusters. MeSH impact factors have
relatively-strong relations with other metrics at the middle panel
in Table 1. Kendall’s W is 0.84 ( 25.29, df = 5, p < 0.001), indicating
a strong IC (at the bottom in Table 1). In Table 1, we can see that
the topic of adverse effects earns the highest IF(=2.06=37/18)
compared to other counterparts. Similarly, the topic of adverse
effects also owns the highest metrics if author-level indices were
The top 10 representatives of author clusters in otolaryngology
are shown in Figure 3. The representatives with the highest
degree centrality for each cluster are highlighted with the author
names. The largest bubble size is the author BW Jafek, followed
by BJ Romanczuk, and LH Weiland. The one-way ANOVA shows
no any difference in impact factor exists among author clusters.
This study found that (i) the most cited authors is P A
Santi(PMID=113741 cited 10 times) with high AIF=10; (ii) the top
three topics are physiology, surgery, and pathology; (iii) the most
number of cited article is entitled by “Arteriolar sclerosis as a cause
of presbycusis” with PMID=113738. Differences in impact factor
were found among MeSH clusters with statistics of F(9,37)=2.287
and p=0.025. Although the h-index [20-25] is a popular authorlevel
metric that can measure both the productivity and citation
impact of the publications of a scientist, one of its shortcomings
is the assumption of equal credits for all coauthors in an article
[26-28]. Many concepts of author impact factor(AIF) has already
proposed before [28-33], but we are not aware of any empirical
study that can successfully solve the problem of quantifying
coauthor contributions  in the empirical discipline.
Even Vavryčuk  proposed a combined weighted counting
scheme in 2018; the weighted mathematical scheme is complex
and not applicable compared to the one in Eq. 1. The most worthnoting
feature in this study is the general AWS fully congruent
with the category probability theory based on the Rasch rating
scale model (RSM) . We can adjust the parameters(i.e., the
base and the power) to accommodate many types of situations
or scenarios in practice. Hence, Vavryčuk’s combined weighted
scheme  (or the harmonic credits ) is a special case of the
general AWS in Eq. 2. Another feature of this study is about the
MeSH clusters classified by the SNA and assigned by the maximum
likelihood estimation through the equation for a given cluster(k)=
. With which, the relations between IF and the article topics can
be inferred, like adverse effect with the highest IF(=2.06=37/18)
compared to others. Besides the author PA Santi(PMID=113741
cited 10 times) with high AIF=10 , the calculation of metrics
can be applied to others, such as the author AF Ryan at the righttop
side in Figure 1 has two citable articles [36,37] cited six and
two times, respectively, with metrics of AIF=4.92, Ag=2.46, h=1.39,
=2, and x=2.09. The topic clusters denoted by the representative
MeSH terms are physiology, surgery, pathology, and so on, see
Figure 2. The second feature is the intrinsic dynamic character
of the simple AIFs to examine the change of author’s AIF. Unlike
the h-index, which is a growing measure taking into account the
whole career path.
Although findings are based on the above analysis, there are
still several potential limitations that may encourage further
research efforts. First, all data were linked to the PubMed
database. There might be some biases of understanding the
matched authors because some different authors with the same
name or abbreviation exist, who are affiliated with different
institutions. Therefore, the result of author relationship analysis
would be influenced by the accuracy of the indexing author.
Second, many algorithms have been used for SNA. We merely
applied the algorithm of degree centrality in the Figures. Any
changes in the algorithm used in this study might present a
different pattern and judgment to the results. Similarly, the
formula, Eq.1, used in this study is also a special case of the
general AWS model. Any change for the parameters might present
a different AIF or other metrics and judgment to the results. Third,
the assumption of corresponding (or supervisory) authors being
the last authors might be challenged, especially in computing AIFs.
Any parameters changed in our proposed formula will affect the
author contribution weights and the AIFs(or h-index) in results.
Fourth, the data extracted from PMC cannot be generalized
to other major citation databases—such as the Scientific Citation
Index (SCI; Thomson Reuters, New York, NY, USA) and Scopus
(Elsevier, Amsterdam, The Netherlands). Such as the most cited
authors are determined by the paper selections on Pubmed.
Finally, the data were merely downloaded in 1978 which are
limited to the generation of study results in a short period.
Authors are recommended to include many years regarding the
topic of otolaryngology in the future.
The AWS-based x-index can be applied to other academic fields
for understanding the most highly cited authors in a discipline.
The AWS can objectively and fairly determine the individual
researchers’ achievements(IRA) in the discernible future.