Thematic maps with scatter and 4-quadrant plots in R to identity dominant entities on schizophrenia in psychiatry since 2017: Bibliometric analysis
Schizophrenia is a psychiatric disorder characterized by positive, negative, and cognitive symptoms.  Approximately 5% of all schizophrenia cases are diagnosed with early-onset schizophrenia (EOS) before the age of 18 years.  Although EOS is considered continuous with its adult-onset counterpart, 2 distinct onset-age cutoffs, 12 and 17, may indicate incrementally worse outcomes in EOS.  The younger the onset age is, the greater the severity of premorbid deficits,  such as educational, social, and cognitive impairments. According to the neurodevelopmental model, schizophrenia is associated with disrupted brain maturation resulting from genetic, perinatal, and environmental factors.  1.1. Bibliographic analysis required for schizophrenia-related articles Those who suffer from EOS and schizophrenia experience premorbid neurocognitive deficits in early childhood. There is a neurodevelopmental delay in the acquisition of cognitive skills (neurodevelopmental model) rather than a cognitive decline of previously acquired skills (neurodegenerative model).  Furthermore, structural and functional brain changes have been observed in patients with first-episode psychosis,  individuals at clinically high risk of schizophrenia,  healthy siblings of schizophrenia patients,  and adolescents with schizophrenia  to support the neurodevelopmental basis of schizophrenia. Bibliometrics refers to a form of statistical analysis that has been widely used to analyze published articles, offering an efficient way to measure the scientific impact of articles by employing mathematical models and techniques in a particular field.  In this type of analysis, entities (e.g., countries, institutions, department, and authors) with the most significant scientific contributions are identified. 1.2. Bibliometric metrics used to evaluate research achievements By the use of Web of Science (WoS)  and the keyword Schizophrenia on research topic in Psychiatry since 2017, approximately 20,606 articles (or review articles) were published. Which authors made the most contributions to the field of schizophrenia remains unknown. We were motivated to explore which article entities (represented by authors, countries, institutes, departments, and journals) recently contributed the most to schizophrenia and then report influential entities. Most articles used bibliometric metrics (e.g., h-index, hx-index, AIF, g-index, x-index, and hT-index) to evaluate individual research achievements (IRAs). [ – ] The disadvantages of using those metrics include assuming all coauthors with equal credits contributed to the article, regardless of document type and journal impact factor. The CAJ score  was used to evaluate the IRAs, without ignoring citations. Thus, the CAJL score involving the L-index  was proposed for measuring IRAs. 1.3. Citation analysis required in bibliometrics Citation counts are frequently and commonly applied to bibliometrics, particularly using article keywords to predict article citations.  Hence, bibliometric analyses can identify study characteristics and article citations related to their keywords in a particular field of study.  In WoS,  a search of titles containing the phrase “100 top-cited” revealed 396 publications. There are 3 categories of information frequently mentioned in these articles: descriptive statistics (DS), significant topics or article types with research domains (RD), and research achievements in entities (RA).  Based on citation analysis, the most influential papers in schizophrenia  have been identified, which differs distinctly from those papers [ – ] as simple as those DS and RD in their studies (i.e., no such RAs were mentioned). 1.4. Visualizations and predictions are lacking in bibliometrics Although those commonly bibliographical studies helped us identify the main features that created an enormous distinction within the field and provided a guideline (on DS, RD, and RA mentioned previously) for physicians and researchers in a particular discipline, 2 perspectives were frequently ignored owing to the lack of a visualization for highlighting all relevant entities on a picture  and a way to predict the number of article citations using medical subject headings (MeSH terms in PubMed) [ , , – ] or using Keywords Plus in WoS. Therefore, several breakthroughs should be made to enhance the bibliographical study. It is required that the CAJL score involving article citations, prediction of article citations using WoS Keywords Plus, and visualizations can display RAs in a concise, and powerful manner (e.g., 4-quadrant radar plot  and thematic maps  ). 1.5. Study aims Our research goals were to explore the leading authors, institutes, countries, and journals on schizophrenia in psychiatry since 2017 using the category, JIF, authorship, and L-index (CJAL) score via 7 unique visualizations based on schizophrenia-related articles indexed in WoS. A hypothesis was examined that the mean number of citations for keywords could predict the number of citations for 100 top-cited articles. 

Those who suffer from EOS and schizophrenia experience premorbid neurocognitive deficits in early childhood. There is a neurodevelopmental delay in the acquisition of cognitive skills (neurodevelopmental model) rather than a cognitive decline of previously acquired skills (neurodegenerative model).  Furthermore, structural and functional brain changes have been observed in patients with first-episode psychosis,  individuals at clinically high risk of schizophrenia,  healthy siblings of schizophrenia patients,  and adolescents with schizophrenia  to support the neurodevelopmental basis of schizophrenia. Bibliometrics refers to a form of statistical analysis that has been widely used to analyze published articles, offering an efficient way to measure the scientific impact of articles by employing mathematical models and techniques in a particular field.  In this type of analysis, entities (e.g., countries, institutions, department, and authors) with the most significant scientific contributions are identified. 

By the use of Web of Science (WoS)  and the keyword Schizophrenia on research topic in Psychiatry since 2017, approximately 20,606 articles (or review articles) were published. Which authors made the most contributions to the field of schizophrenia remains unknown. We were motivated to explore which article entities (represented by authors, countries, institutes, departments, and journals) recently contributed the most to schizophrenia and then report influential entities. Most articles used bibliometric metrics (e.g., h-index, hx-index, AIF, g-index, x-index, and hT-index) to evaluate individual research achievements (IRAs). [ – ] The disadvantages of using those metrics include assuming all coauthors with equal credits contributed to the article, regardless of document type and journal impact factor. The CAJ score  was used to evaluate the IRAs, without ignoring citations. Thus, the CAJL score involving the L-index  was proposed for measuring IRAs. 

Citation counts are frequently and commonly applied to bibliometrics, particularly using article keywords to predict article citations.  Hence, bibliometric analyses can identify study characteristics and article citations related to their keywords in a particular field of study.  In WoS,  a search of titles containing the phrase “100 top-cited” revealed 396 publications. There are 3 categories of information frequently mentioned in these articles: descriptive statistics (DS), significant topics or article types with research domains (RD), and research achievements in entities (RA).  Based on citation analysis, the most influential papers in schizophrenia  have been identified, which differs distinctly from those papers [ – ] as simple as those DS and RD in their studies (i.e., no such RAs were mentioned). 

Although those commonly bibliographical studies helped us identify the main features that created an enormous distinction within the field and provided a guideline (on DS, RD, and RA mentioned previously) for physicians and researchers in a particular discipline, 2 perspectives were frequently ignored owing to the lack of a visualization for highlighting all relevant entities on a picture  and a way to predict the number of article citations using medical subject headings (MeSH terms in PubMed) [ , , – ] or using Keywords Plus in WoS. Therefore, several breakthroughs should be made to enhance the bibliographical study. It is required that the CAJL score involving article citations, prediction of article citations using WoS Keywords Plus, and visualizations can display RAs in a concise, and powerful manner (e.g., 4-quadrant radar plot  and thematic maps  ). 

Our research goals were to explore the leading authors, institutes, countries, and journals on schizophrenia in psychiatry since 2017 using the category, JIF, authorship, and L-index (CJAL) score via 7 unique visualizations based on schizophrenia-related articles indexed in WoS. A hypothesis was examined that the mean number of citations for keywords could predict the number of citations for 100 top-cited articles. 

2.1. Data source As of August 24, 2022, 20,606 abstracts published on the topic of schizophrenia in psychiatry since 2017 were retrieved from the WoS. This study included only articles and review articles. An extensive collection of metadata was gathered, including author names, research institutes, departments, countries of origin, and article citations. All data deposited in Supplemental Digital Content 1, http://links.lww.com/MD/K687 are publicly released on WoS without participant identification information. Ethical approval was thus waived. 2.2. Four analytics applied to this study 2.2.1. Descriptive analytics in 20,606 articles. In this study, the Sankey diagram  was applied to display the top 5 elements in article entities, including publication years, countries, institutes, departments, authors, journals, and the types of documents denoted by nodes and edges in their relations. A choropleth map  was used to depict the geographical distribution of publications within countries/regions, where the US states and provinces/metropolitan cities in China were divided into regions compared with other countries/regions with similar research populations. The citations and publications for schizophrenia journals in psychiatry were displayed on a scatter chart, with the bubble sizes based on the mean number of citations (=citations/publications). 2.2.1.1. The 4-quadrant plot. A 4-quadrant radar plot was applied to display authors’ research achievements (RAs) based on Y-index  and CJAL score.  The RAs were evaluated by the number of publications in the positions of corresponding and first (co-first) authors using the Y-index  in equation 1. The radar plot  was drawn based on the Yk-index (=as the radius in a specific quadrant) in equation 2. Y − i n d e x = F P + R P f r o m 1 t o ∝ , (1) Y k = n o r m a l i z e d Y − i n d e x ( f r o m 0 t o 1 ) = Y i Y A (2) where RP (publication of corresponding author) and FP (publication of first author) denote the publications from corresponding and co-first authors, Y is the largest Y-index in a group, and Yi is the Y-index of a specific member in comparison. The radar chart can be drawn with radiuses (=Yk-index of 0.1, 0.3, 0.5, and 0.7, respectively)  shown in 4 quadrants. Details about the drawing of radar plot in R programing language are in Supplemental Digital Content 2, http://links.lww.com/MD/K688 . 2.2.1.2. The CJAL score. In many medical schools in Taiwan, the minimal requirement for faculty appointment and promotion (FAP) was calculated using a quantitative CJA score.  Three factors are considered in the CJA scores for a published article: the Category (C; e.g., review, original article, case report, etc), the journal “quality” (J; e.g., impact factor, JIF, or ranking of the journal), and the authorship order denoted by A). A publication final score is calculated by multiplying each of these 3 aspects (Equation 3). CJA scores original research articles higher than other types of manuscripts; co-first authors score higher than other collaborators; for the journal quality assessment, they use the JIF or SCI/SSCI journal rankings for SCI/SSCI-indexed papers.  SCI/SSCI journal rankings are based on JIF in each research domain; therefore, domain-specific journal rankings are usually not significantly different from those based on the journal impact factor (JIF).  C J A s c o r e = ∑ n i = 1 C i × J i × A i , (3) C J A L s c o r e = ∑ n i = 1 C i × J i × A i × L − i n d e x i , (4) L - i n d e x = r o u n d ( l o g ( C i t a t i o n A n × A g e + 1 ) , 0 ) , >= 1 (5) The final CJA is computed by equation 4 by adding an additional L-index  in equation 5 to compute the CJAL in Equation 4. Through the computation of absolute advantage coefficient(AAC),  the dominance strength in a variable (i.e., country, journal, category, or CIDA) can be measured and judged by the effect size, with criteria of < 0.5, between 0.5 and 0.7, and not <0.7 as the small, medium, and large effect sizes, respectively.  As such, The 4-quadrant radar plot was applied to display the RAs for the top 10 elements in article entities (i.e., CIDA). Bubbles were sized by the CJAL score. 2.2.2. Diagnostic analytics. 2.2.2.1. Collaborative map on country-based author. The follower-leading cluster algorithm (FLCA) [ – ] was applied to classify clusters. A collaborative map with 4 quadrants (namely, emerging, niche, co-work (or co-word), and motor by nodes’ edge degree and frequency degree, respectively, in network) was made in R programing language  generated on a R platform,  based on country-based author collaborations and keyword cooccurrences, respectively. 2.2.2.2. Thematic map on word cooccurrences. One hundred top-cited articles related to schizophrenia in psychiatry (T100SCHIZ)  were extracted from the current study. Keywords Plus in WoS was used to cluster research domains using the FLCA, [ – ] as displayed in social network analysis (SNA). [ – ] Keywords with the highest weighted score in the respective cluster using SNA in the previous section would be assigned as the representative of the cluster. Each article can be classified into the subject category based on their keywords via equation 7.  s u b j e c t c a t e g o r y = A t [ max 0 ≤ x ≤ 1 ∑ L i = 1 ∑ n j = 1 ( m = m + 1 ) k ∈ F ] (7) where L is the number of keywords in article i. n is the number of keywords denoted by keyword k belonging to the subject category defined in SNA mentioned in a previous study.  Accordingly, the subject category is redirected to the maximal number of keywords (=m) involved in the cluster via equation 7. 2.2.3. Predictive analytics in T100SCHIZ. The number of connections for a specific keyword plus was computed based on an equal weight in an article.  The weighted mean citation for each term was applied to predict article citations based on t he correlation coefficient (CC) in determining the predictive power related to original article citations. The CC t value was calculated using the following formula ( = C C × n − 2 1 − C C × C C ) .  A prediction equation was produced through the simple regression analysis. A scatter plot with 95% control lines  was used to display the relationship between weighted mean citations and article citations in T100SCHIZ. 2.2.4. Prescriptive analytics in T100SCHIZ. The impact beam plot (IBP)  was applied to display T100SCHIZ.  With the IBP, the most worth-reading articles with more number of citations were highlighted by the bubble size. The publication years are on horizontal axis and the themes on vertical axis. The definitions of these themes have been assigned to each article based on the equation 4. All those articles could be linked to PubMed once the dot representing an article in IBP was selected and clicked. 2.3. Statistics and tools The correlation coefficient (r) was used to determine the predictive power between the weighted keywords and original article citations. The r t value was calculated using the following formula ( = r × n − 2 1 − r × r ) .  A prediction equation was produced through simple regression analysis using MedCalc statistical software, version 9.5.0.0 (MedCalc, New York, New York, USA). The significance level was set at Type I error (0.05). All graphs were drawn by author-made modules in Excel (Microsoft Corp). We created pages of HTML used for Google Maps. All relevant CJAL scores and Y-indices for each member on the 4-quadrant radar plot can be linked to dashboards on Google Maps. The study process is described in Supplemental Digital Content 2, http://links.lww.com/MD/K688 . 

As of August 24, 2022, 20,606 abstracts published on the topic of schizophrenia in psychiatry since 2017 were retrieved from the WoS. This study included only articles and review articles. An extensive collection of metadata was gathered, including author names, research institutes, departments, countries of origin, and article citations. All data deposited in Supplemental Digital Content 1, http://links.lww.com/MD/K687 are publicly released on WoS without participant identification information. Ethical approval was thus waived. 

2.2.1. Descriptive analytics in 20,606 articles. In this study, the Sankey diagram  was applied to display the top 5 elements in article entities, including publication years, countries, institutes, departments, authors, journals, and the types of documents denoted by nodes and edges in their relations. A choropleth map  was used to depict the geographical distribution of publications within countries/regions, where the US states and provinces/metropolitan cities in China were divided into regions compared with other countries/regions with similar research populations. The citations and publications for schizophrenia journals in psychiatry were displayed on a scatter chart, with the bubble sizes based on the mean number of citations (=citations/publications). 2.2.1.1. The 4-quadrant plot. A 4-quadrant radar plot was applied to display authors’ research achievements (RAs) based on Y-index  and CJAL score.  The RAs were evaluated by the number of publications in the positions of corresponding and first (co-first) authors using the Y-index  in equation 1. The radar plot  was drawn based on the Yk-index (=as the radius in a specific quadrant) in equation 2. Y − i n d e x = F P + R P f r o m 1 t o ∝ , (1) Y k = n o r m a l i z e d Y − i n d e x ( f r o m 0 t o 1 ) = Y i Y A (2) where RP (publication of corresponding author) and FP (publication of first author) denote the publications from corresponding and co-first authors, Y is the largest Y-index in a group, and Yi is the Y-index of a specific member in comparison. The radar chart can be drawn with radiuses (=Yk-index of 0.1, 0.3, 0.5, and 0.7, respectively)  shown in 4 quadrants. Details about the drawing of radar plot in R programing language are in Supplemental Digital Content 2, http://links.lww.com/MD/K688 . 2.2.1.2. The CJAL score. In many medical schools in Taiwan, the minimal requirement for faculty appointment and promotion (FAP) was calculated using a quantitative CJA score.  Three factors are considered in the CJA scores for a published article: the Category (C; e.g., review, original article, case report, etc), the journal “quality” (J; e.g., impact factor, JIF, or ranking of the journal), and the authorship order denoted by A). A publication final score is calculated by multiplying each of these 3 aspects (Equation 3). CJA scores original research articles higher than other types of manuscripts; co-first authors score higher than other collaborators; for the journal quality assessment, they use the JIF or SCI/SSCI journal rankings for SCI/SSCI-indexed papers.  SCI/SSCI journal rankings are based on JIF in each research domain; therefore, domain-specific journal rankings are usually not significantly different from those based on the journal impact factor (JIF).  C J A s c o r e = ∑ n i = 1 C i × J i × A i , (3) C J A L s c o r e = ∑ n i = 1 C i × J i × A i × L − i n d e x i , (4) L - i n d e x = r o u n d ( l o g ( C i t a t i o n A n × A g e + 1 ) , 0 ) , >= 1 (5) The final CJA is computed by equation 4 by adding an additional L-index  in equation 5 to compute the CJAL in Equation 4. Through the computation of absolute advantage coefficient(AAC),  the dominance strength in a variable (i.e., country, journal, category, or CIDA) can be measured and judged by the effect size, with criteria of < 0.5, between 0.5 and 0.7, and not <0.7 as the small, medium, and large effect sizes, respectively.  As such, The 4-quadrant radar plot was applied to display the RAs for the top 10 elements in article entities (i.e., CIDA). Bubbles were sized by the CJAL score. 2.2.2. Diagnostic analytics. 2.2.2.1. Collaborative map on country-based author. The follower-leading cluster algorithm (FLCA) [ – ] was applied to classify clusters. A collaborative map with 4 quadrants (namely, emerging, niche, co-work (or co-word), and motor by nodes’ edge degree and frequency degree, respectively, in network) was made in R programing language  generated on a R platform,  based on country-based author collaborations and keyword cooccurrences, respectively. 2.2.2.2. Thematic map on word cooccurrences. One hundred top-cited articles related to schizophrenia in psychiatry (T100SCHIZ)  were extracted from the current study. Keywords Plus in WoS was used to cluster research domains using the FLCA, [ – ] as displayed in social network analysis (SNA). [ – ] Keywords with the highest weighted score in the respective cluster using SNA in the previous section would be assigned as the representative of the cluster. Each article can be classified into the subject category based on their keywords via equation 7.  s u b j e c t c a t e g o r y = A t [ max 0 ≤ x ≤ 1 ∑ L i = 1 ∑ n j = 1 ( m = m + 1 ) k ∈ F ] (7) where L is the number of keywords in article i. n is the number of keywords denoted by keyword k belonging to the subject category defined in SNA mentioned in a previous study.  Accordingly, the subject category is redirected to the maximal number of keywords (=m) involved in the cluster via equation 7. 2.2.3. Predictive analytics in T100SCHIZ. The number of connections for a specific keyword plus was computed based on an equal weight in an article.  The weighted mean citation for each term was applied to predict article citations based on t he correlation coefficient (CC) in determining the predictive power related to original article citations. The CC t value was calculated using the following formula ( = C C × n − 2 1 − C C × C C ) .  A prediction equation was produced through the simple regression analysis. A scatter plot with 95% control lines  was used to display the relationship between weighted mean citations and article citations in T100SCHIZ. 2.2.4. Prescriptive analytics in T100SCHIZ. The impact beam plot (IBP)  was applied to display T100SCHIZ.  With the IBP, the most worth-reading articles with more number of citations were highlighted by the bubble size. The publication years are on horizontal axis and the themes on vertical axis. The definitions of these themes have been assigned to each article based on the equation 4. All those articles could be linked to PubMed once the dot representing an article in IBP was selected and clicked. 

In this study, the Sankey diagram  was applied to display the top 5 elements in article entities, including publication years, countries, institutes, departments, authors, journals, and the types of documents denoted by nodes and edges in their relations. A choropleth map  was used to depict the geographical distribution of publications within countries/regions, where the US states and provinces/metropolitan cities in China were divided into regions compared with other countries/regions with similar research populations. The citations and publications for schizophrenia journals in psychiatry were displayed on a scatter chart, with the bubble sizes based on the mean number of citations (=citations/publications). 2.2.1.1. The 4-quadrant plot. A 4-quadrant radar plot was applied to display authors’ research achievements (RAs) based on Y-index  and CJAL score.  The RAs were evaluated by the number of publications in the positions of corresponding and first (co-first) authors using the Y-index  in equation 1. The radar plot  was drawn based on the Yk-index (=as the radius in a specific quadrant) in equation 2. Y − i n d e x = F P + R P f r o m 1 t o ∝ , (1) Y k = n o r m a l i z e d Y − i n d e x ( f r o m 0 t o 1 ) = Y i Y A (2) where RP (publication of corresponding author) and FP (publication of first author) denote the publications from corresponding and co-first authors, Y is the largest Y-index in a group, and Yi is the Y-index of a specific member in comparison. The radar chart can be drawn with radiuses (=Yk-index of 0.1, 0.3, 0.5, and 0.7, respectively)  shown in 4 quadrants. Details about the drawing of radar plot in R programing language are in Supplemental Digital Content 2, http://links.lww.com/MD/K688 . 2.2.1.2. The CJAL score. In many medical schools in Taiwan, the minimal requirement for faculty appointment and promotion (FAP) was calculated using a quantitative CJA score.  Three factors are considered in the CJA scores for a published article: the Category (C; e.g., review, original article, case report, etc), the journal “quality” (J; e.g., impact factor, JIF, or ranking of the journal), and the authorship order denoted by A). A publication final score is calculated by multiplying each of these 3 aspects (Equation 3). CJA scores original research articles higher than other types of manuscripts; co-first authors score higher than other collaborators; for the journal quality assessment, they use the JIF or SCI/SSCI journal rankings for SCI/SSCI-indexed papers.  SCI/SSCI journal rankings are based on JIF in each research domain; therefore, domain-specific journal rankings are usually not significantly different from those based on the journal impact factor (JIF).  C J A s c o r e = ∑ n i = 1 C i × J i × A i , (3) C J A L s c o r e = ∑ n i = 1 C i × J i × A i × L − i n d e x i , (4) L - i n d e x = r o u n d ( l o g ( C i t a t i o n A n × A g e + 1 ) , 0 ) , >= 1 (5) The final CJA is computed by equation 4 by adding an additional L-index  in equation 5 to compute the CJAL in Equation 4. Through the computation of absolute advantage coefficient(AAC),  the dominance strength in a variable (i.e., country, journal, category, or CIDA) can be measured and judged by the effect size, with criteria of < 0.5, between 0.5 and 0.7, and not <0.7 as the small, medium, and large effect sizes, respectively.  As such, The 4-quadrant radar plot was applied to display the RAs for the top 10 elements in article entities (i.e., CIDA). Bubbles were sized by the CJAL score. 

A 4-quadrant radar plot was applied to display authors’ research achievements (RAs) based on Y-index  and CJAL score.  The RAs were evaluated by the number of publications in the positions of corresponding and first (co-first) authors using the Y-index  in equation 1. The radar plot  was drawn based on the Yk-index (=as the radius in a specific quadrant) in equation 2. Y − i n d e x = F P + R P f r o m 1 t o ∝ , (1) Y k = n o r m a l i z e d Y − i n d e x ( f r o m 0 t o 1 ) = Y i Y A (2) where RP (publication of corresponding author) and FP (publication of first author) denote the publications from corresponding and co-first authors, Y is the largest Y-index in a group, and Yi is the Y-index of a specific member in comparison. The radar chart can be drawn with radiuses (=Yk-index of 0.1, 0.3, 0.5, and 0.7, respectively)  shown in 4 quadrants. Details about the drawing of radar plot in R programing language are in Supplemental Digital Content 2, http://links.lww.com/MD/K688 . 

In many medical schools in Taiwan, the minimal requirement for faculty appointment and promotion (FAP) was calculated using a quantitative CJA score.  Three factors are considered in the CJA scores for a published article: the Category (C; e.g., review, original article, case report, etc), the journal “quality” (J; e.g., impact factor, JIF, or ranking of the journal), and the authorship order denoted by A). A publication final score is calculated by multiplying each of these 3 aspects (Equation 3). CJA scores original research articles higher than other types of manuscripts; co-first authors score higher than other collaborators; for the journal quality assessment, they use the JIF or SCI/SSCI journal rankings for SCI/SSCI-indexed papers.  SCI/SSCI journal rankings are based on JIF in each research domain; therefore, domain-specific journal rankings are usually not significantly different from those based on the journal impact factor (JIF).  C J A s c o r e = ∑ n i = 1 C i × J i × A i , (3) C J A L s c o r e = ∑ n i = 1 C i × J i × A i × L − i n d e x i , (4) L - i n d e x = r o u n d ( l o g ( C i t a t i o n A n × A g e + 1 ) , 0 ) , >= 1 (5) The final CJA is computed by equation 4 by adding an additional L-index  in equation 5 to compute the CJAL in Equation 4. Through the computation of absolute advantage coefficient(AAC),  the dominance strength in a variable (i.e., country, journal, category, or CIDA) can be measured and judged by the effect size, with criteria of < 0.5, between 0.5 and 0.7, and not <0.7 as the small, medium, and large effect sizes, respectively.  As such, The 4-quadrant radar plot was applied to display the RAs for the top 10 elements in article entities (i.e., CIDA). Bubbles were sized by the CJAL score. 

2.2.2.1. Collaborative map on country-based author. The follower-leading cluster algorithm (FLCA) [ – ] was applied to classify clusters. A collaborative map with 4 quadrants (namely, emerging, niche, co-work (or co-word), and motor by nodes’ edge degree and frequency degree, respectively, in network) was made in R programing language  generated on a R platform,  based on country-based author collaborations and keyword cooccurrences, respectively. 2.2.2.2. Thematic map on word cooccurrences. One hundred top-cited articles related to schizophrenia in psychiatry (T100SCHIZ)  were extracted from the current study. Keywords Plus in WoS was used to cluster research domains using the FLCA, [ – ] as displayed in social network analysis (SNA). [ – ] Keywords with the highest weighted score in the respective cluster using SNA in the previous section would be assigned as the representative of the cluster. Each article can be classified into the subject category based on their keywords via equation 7.  s u b j e c t c a t e g o r y = A t [ max 0 ≤ x ≤ 1 ∑ L i = 1 ∑ n j = 1 ( m = m + 1 ) k ∈ F ] (7) where L is the number of keywords in article i. n is the number of keywords denoted by keyword k belonging to the subject category defined in SNA mentioned in a previous study.  Accordingly, the subject category is redirected to the maximal number of keywords (=m) involved in the cluster via equation 7. 

The follower-leading cluster algorithm (FLCA) [ – ] was applied to classify clusters. A collaborative map with 4 quadrants (namely, emerging, niche, co-work (or co-word), and motor by nodes’ edge degree and frequency degree, respectively, in network) was made in R programing language  generated on a R platform,  based on country-based author collaborations and keyword cooccurrences, respectively. 

One hundred top-cited articles related to schizophrenia in psychiatry (T100SCHIZ)  were extracted from the current study. Keywords Plus in WoS was used to cluster research domains using the FLCA, [ – ] as displayed in social network analysis (SNA). [ – ] Keywords with the highest weighted score in the respective cluster using SNA in the previous section would be assigned as the representative of the cluster. Each article can be classified into the subject category based on their keywords via equation 7.  s u b j e c t c a t e g o r y = A t [ max 0 ≤ x ≤ 1 ∑ L i = 1 ∑ n j = 1 ( m = m + 1 ) k ∈ F ] (7) where L is the number of keywords in article i. n is the number of keywords denoted by keyword k belonging to the subject category defined in SNA mentioned in a previous study.  Accordingly, the subject category is redirected to the maximal number of keywords (=m) involved in the cluster via equation 7. 

The number of connections for a specific keyword plus was computed based on an equal weight in an article.  The weighted mean citation for each term was applied to predict article citations based on t he correlation coefficient (CC) in determining the predictive power related to original article citations. The CC t value was calculated using the following formula ( = C C × n − 2 1 − C C × C C ) .  A prediction equation was produced through the simple regression analysis. A scatter plot with 95% control lines  was used to display the relationship between weighted mean citations and article citations in T100SCHIZ. 

The impact beam plot (IBP)  was applied to display T100SCHIZ.  With the IBP, the most worth-reading articles with more number of citations were highlighted by the bubble size. The publication years are on horizontal axis and the themes on vertical axis. The definitions of these themes have been assigned to each article based on the equation 4. All those articles could be linked to PubMed once the dot representing an article in IBP was selected and clicked. 

The correlation coefficient (r) was used to determine the predictive power between the weighted keywords and original article citations. The r t value was calculated using the following formula ( = r × n − 2 1 − r × r ) .  A prediction equation was produced through simple regression analysis using MedCalc statistical software, version 9.5.0.0 (MedCalc, New York, New York, USA). The significance level was set at Type I error (0.05). All graphs were drawn by author-made modules in Excel (Microsoft Corp). We created pages of HTML used for Google Maps. All relevant CJAL scores and Y-indices for each member on the 4-quadrant radar plot can be linked to dashboards on Google Maps. The study process is described in Supplemental Digital Content 2, http://links.lww.com/MD/K688 . 

3.1. Descriptive analytics The most productive members in each article entity are the year 2018 (3850), the US (4096), Kings College London (UK) (497), Schizophr. Res. (1681), Article (17207), Psychiatry (3497), and Sandeep Grover (India) in years, countries, institutes, journals, document types, departments, and authors, respectively, as shown on the Sankey diagram in Figure . The top 5 countries of the UK, Australia, Germany, Canada, and Spain dominate the field of schizophrenia in psychiatry, as shown in Figure , based on countries/regions. In particular, the US states and provinces/metropolitan cities in China are divided to compare their publications with other countries/regions with similar research populations. The 3 most productive journals are Schizophr. Res. (1681), Schizophr. Bull (745), and Mol. Psychiatr. (430), as shown in Figure . The journal World Psychiatry earns the highest mean citation (=101.44). Based on the CJAL scores, the US (18861), Kings College London (U.S. (2572), Psychiatry (14603), and Kolanu Nithin (Australia) (9.88) had the highest scores in countries, institutes, departments, and authors (Fig. ). Readers are encouraged to scan the QR code and click on the bubble of interest to examine the detailed information on Google Maps. Only the Kings College London in institutes has a substantially large effect  in dominance over the next 2 counterparts due to the AAC equal to 0.73 (>0.70). 3.2. Diagnostic analytics From a total of 20,606 articles, Figure showcases 3 distinct clusters representing the top 20 countries with the highest levels of author collaborations. The United States falls under the “motor” category, while the United Kingdom and Australia are categorized as “co-work.” The remaining 17 countries are classified as either “emerging” or “declining.” In relation to T100SCHIZ, a mere 97 articles could be extracted due to a mismatch in PMIDs between PubMed and articles in WoS. Figure maps out the 3 themes identified using the FLCA algorithm and links them to T100SCHIZ, including SHIZOPHRENIA, METAANALYSIS, and VERSION. A comprehensive breakdown of the classification and assignment methodology can be found in Supplement Digital Content 2, http://links.lww.com/MD/K688 3.3. Predictive analytics Based on the keyword weighted citations(defined as IFWCD  ), keywords are evident in the predictive power of the number of article citations (i.e., R = 0.97, df = 98, t = 38.36, P < .001). The regression equation is defined as article citation (y = −70.48 + 1.34 × weight (x) of keywords. The slope coefficient was statistically significant (F = 1471.74; P < .001), as shown in Figure . 3.4. Prescriptive analytics The IBP shown in Figure , which includes T100SCHZ articles, indicates that articles with the theme of SHIZOPHRENIA accounts for 73%. The most worth-reading articles with more number of citations were highlighted by the symbols A and B near to the bubbles. In T100SCHZ, the h-index  reaches 75 and x-index  at 96.86. The article appears on PubMed immediately after clicking the bubble of interest. 3.5. Online dashboards shown on google maps All the QR codes in Figures are linked to the dashboards. Readers are suggested to examine the displayed dashboards on Google Maps. 

The most productive members in each article entity are the year 2018 (3850), the US (4096), Kings College London (UK) (497), Schizophr. Res. (1681), Article (17207), Psychiatry (3497), and Sandeep Grover (India) in years, countries, institutes, journals, document types, departments, and authors, respectively, as shown on the Sankey diagram in Figure . The top 5 countries of the UK, Australia, Germany, Canada, and Spain dominate the field of schizophrenia in psychiatry, as shown in Figure , based on countries/regions. In particular, the US states and provinces/metropolitan cities in China are divided to compare their publications with other countries/regions with similar research populations. The 3 most productive journals are Schizophr. Res. (1681), Schizophr. Bull (745), and Mol. Psychiatr. (430), as shown in Figure . The journal World Psychiatry earns the highest mean citation (=101.44). Based on the CJAL scores, the US (18861), Kings College London (U.S. (2572), Psychiatry (14603), and Kolanu Nithin (Australia) (9.88) had the highest scores in countries, institutes, departments, and authors (Fig. ). Readers are encouraged to scan the QR code and click on the bubble of interest to examine the detailed information on Google Maps. Only the Kings College London in institutes has a substantially large effect  in dominance over the next 2 counterparts due to the AAC equal to 0.73 (>0.70). 

From a total of 20,606 articles, Figure showcases 3 distinct clusters representing the top 20 countries with the highest levels of author collaborations. The United States falls under the “motor” category, while the United Kingdom and Australia are categorized as “co-work.” The remaining 17 countries are classified as either “emerging” or “declining.” In relation to T100SCHIZ, a mere 97 articles could be extracted due to a mismatch in PMIDs between PubMed and articles in WoS. Figure maps out the 3 themes identified using the FLCA algorithm and links them to T100SCHIZ, including SHIZOPHRENIA, METAANALYSIS, and VERSION. A comprehensive breakdown of the classification and assignment methodology can be found in Supplement Digital Content 2, http://links.lww.com/MD/K688 

Based on the keyword weighted citations(defined as IFWCD  ), keywords are evident in the predictive power of the number of article citations (i.e., R = 0.97, df = 98, t = 38.36, P < .001). The regression equation is defined as article citation (y = −70.48 + 1.34 × weight (x) of keywords. The slope coefficient was statistically significant (F = 1471.74; P < .001), as shown in Figure . 

The IBP shown in Figure , which includes T100SCHZ articles, indicates that articles with the theme of SHIZOPHRENIA accounts for 73%. The most worth-reading articles with more number of citations were highlighted by the symbols A and B near to the bubbles. In T100SCHZ, the h-index  reaches 75 and x-index  at 96.86. The article appears on PubMed immediately after clicking the bubble of interest. 

All the QR codes in Figures are linked to the dashboards. Readers are suggested to examine the displayed dashboards on Google Maps. 

A bibliometric analysis was conducted to verify the hypothesis that the mean number of citations for keywords can predict the number of citations for 100 top-cited articles. The results indicate that (1) the US (18861), Kings College London (U.S. (2572), Psychiatry (14603), and Kolanu Nithin (Australia) (9.88) had the highest CJAL scores in countries, institutes, departments, and authors. The journal of Schizophrenia Res had higher citations (19,017), counts (1681), and mean citations (11.31) in journals, and (2) there was a significant correlation between article citations and weighted keywords (F = 1471.74; P < .001). Accordingly, the research goals have been reached: to explore the leading CIDA entities and journals on schizophrenia in psychiatry since 2017 and to confirm the hypothesis that the mean number of citations for keywords could predict the number of citations for T100SCHZ. 4.1. Additional information There are 3 categories of information frequently mentioned in bibliographic studies  : DS, significant topics or article types with RD, and RAs in entities,  as we did in Figures to 6. Although those (i.e., DS, RD, and RA) helped us identify the main features that created an enormous distinction within a discipline (or a field) and provided a guideline for physicians and researchers on a research topic, 2 perspectives were frequently ignored owing to the lack of a visualization for highlighting all relevant entities on a picture  and a way to predict the number of article citations for the future, [ , – ] as we did in Figure . As such, the most influential papers in schizophrenia  have been illustrated in the literature. However, those results are similar to these studies [ – ] solely describing DS and RD in their studies and differ from the current study (i.e., no such RAs were mentioned), including citation prediction in a study. This study consists of 2 major components. The T100SCHIZ accounts for 0.49% (=100/20606) of the collected sample, unlike many other articles analyzing only the 100 top-cited articles and excluding others related to the topic. The 2 parts in a study of 100 top-cited articles are worthy of imitation in future biometrics studies. T100SCHIs from WoS were categorized into 3 categories based on their characteristics denoted by Keywords Plus. We used SNA to determine the features and subject categories in T100SCHZs. Using these concise diagrams, schizophrenia may find relevant articles more efficiently, facilitating evidence-based decision-making for patients with schizophrenia. SNA was used to determine the article subject categories associated with frequent citations. A combination of both publications and citations contributed to the IFWCD by highlighting the influential keywords on a thematic maps (Fig. ), as presented in previous articles. [ , – , , – ] There may be a reason for this result because physicians in schizophrenia must realize that T100SCIZ and related articles with higher CJAL scores are present in the current study. Of the common major topic keywords listed in these top-cited articles, the prefrontal cortex (301 = IFWCD) had the highest IFWCD, followed by schizophrenia (214.95) and 1st-episode psychosis (212.64) based on Equation 6. This may be partly attributed to the critical role of diagnosis revealed in T100SCHZs. This article applied SNA to describe the classification of T100SCHZs. With these classifications, physicians in schizophrenia may use the classification as an international communication tool to discuss any topic regarding schizophrenia. From our point of view, the classification approach can be applied to other scientific studies, not limited to schizophrenia. The Sankey diagram [ , , ] has been applied to bibliometric analysis in recent years. The Sankey is appropriate for the graph due to categorical dimensions instead of steps (or years) on the x-axis, nodes vertically on the y-axis, and edges correlated with the momentum between adjacent entities. Additionally, the reasons for demonstrating the CJAL score in this study are because the CJA score  takes 3 factors into account: the Category (C; e.g., review, original article, case report, etc), the journal “quality” (J; e.g., impact factor, JIF, or ranking of the journal), and the authorship order denoted by A); L-index  involving article citations. A publication final score is calculated by multiplying each of these 4 aspects (Equation 4). To date, no studies related to schizophrenia based on the CJAL score [ – ] have been identified in WoS. The current study on T100SCHZ is the first to use bibliometric analysis in the schizophrenia field. In Figure , a dashboard-type 4-quadrant radar plot provides overall information rather than those with many tables and graphs in traditional bibliometrics (e.g., 15 Tables and 27 Figures in a study  ). This is a unique and modern approach never seen before in the literature. Bibliometric analysis can be advanced in this manner in the future. 4.2. Three most-cited articles The most-cited article in T100SCHZ was written by Correll et al (2017),  as marked by symbols A and B in Figure . This study was published in World Psychiatry, obtained 685 citations, and categorized as “schizophrenia.” This large-scale meta-analysis confirms that severe mental illness (SMI) patients have a significantly increased risk of cardiovascular disease (CVD) and CVD-related mortality and that elevated body mass indexes, the use of antipsychotics, and the screening and management of CVD require immediate clinical attention. The second most highly cited article was written by Huang et al (2019),  obtained 634 citations, and was published in Lancet Psychiatry, which was classified as “MENTAL-DISORDERS” in our study. According to this study, the prevalence of most mental disorders in China in 2013 was higher than that in 1982, 1993, and 2002 but lower than that in 2009. It is evident from this survey that there are serious challenges related to the high burden of disease identified, but it also offers valuable opportunities for policy makers and health-care professionals to investigate and address the factors that affect mental health in China. The third most-cited article appeared in Moreno et al (2020),  obtained 522 citations, was published in Lancet Psychiatry, and was classified as “SCHIZOPHRENIA” in our study. This article addresses that a group of clinicians, mental health experts, and users of mental health services have come together to discuss the challenges COVID-19 poses to mental health. In addition to providing the infrastructure to address previous system failings by disseminating good practices, the interconnectedness of the world makes society vulnerable to this infection. As a result, mental health care can be sustained, efficient, and equitable. Consequently, the COVID-19 pandemic may offer an opportunity to improve mental health care. 4.3. Implications and changes This study has several noteworthy features. In the first instance, the CJAL score with decimal places can enhance the original h-index in terms of identifying the research accomplishments and rankings of a given group.  To measure the achievements of researchers and research institutions, we proposed using the CJAL score. The second feature is that the Sankey diagram was used to highlight a few vital entities and proved to be viable and feasible in bibliometrics. The third feature is the use of thematic (and collaborative) maps with a 4-quadrant radar plot and a scatter plot, providing readers with a representation of 4 perspectives in article entities with a glance view, particularly with research achievements denoted by the CJAL score instead of the Y-index in traditional studies  in a traditional manner. The scatter chart is a useful tool for predicting article citations using keyword weights, which might be applicable to future bibliometric analyses. In addition, the classification of subject categories (or themes defined in this study) using SNA is objective and unique when compared to previous studies using manual methods  or document types determined by PubMed.  The evidence suggests that the classification method is valid and worth recommending to future researchers. 4.4. Limitations and suggestions Further research should examine a number of issues. The first concern is that the software used to draw the Sankey (or thematic map) is not unique and irreplaceable. Several other software packages make it easy to draw the Sankey online. Second, dashboards in this study are displayed on Google Maps.  These installments are not free of charge because Google Maps requires a paid project key for using the cloud platform. Therefore, it is difficult for other authors to replicate the usage in a short period of time. Third, this study requires considerable computation to calculate the CJAL score. In the future, dedicated software will be required for development. Fourth, although the radar plot in this study was produced in R, the research achievements are determined by many other factors that should be considered when drawing the radar plot in the future. Fifth, only a few dimensions were selected in the Sankey diagram. Other important categories (e.g., journals, subject categories, and T100SCHZ) are required to display on the Sankey simultaneously. Future studies are recommended to involve more dimensions on the x-axis on the Sankey diagram. Finally, although T100SCHZs were extracted mainly from WoS, the results were different in articles retrieved from other databases (e.g., Google Scholar, Scopus, and PubMed). Future studies are required to extract T100SCHZ from more bibliometric databases. 

There are 3 categories of information frequently mentioned in bibliographic studies  : DS, significant topics or article types with RD, and RAs in entities,  as we did in Figures to 6. Although those (i.e., DS, RD, and RA) helped us identify the main features that created an enormous distinction within a discipline (or a field) and provided a guideline for physicians and researchers on a research topic, 2 perspectives were frequently ignored owing to the lack of a visualization for highlighting all relevant entities on a picture  and a way to predict the number of article citations for the future, [ , – ] as we did in Figure . As such, the most influential papers in schizophrenia  have been illustrated in the literature. However, those results are similar to these studies [ – ] solely describing DS and RD in their studies and differ from the current study (i.e., no such RAs were mentioned), including citation prediction in a study. This study consists of 2 major components. The T100SCHIZ accounts for 0.49% (=100/20606) of the collected sample, unlike many other articles analyzing only the 100 top-cited articles and excluding others related to the topic. The 2 parts in a study of 100 top-cited articles are worthy of imitation in future biometrics studies. T100SCHIs from WoS were categorized into 3 categories based on their characteristics denoted by Keywords Plus. We used SNA to determine the features and subject categories in T100SCHZs. Using these concise diagrams, schizophrenia may find relevant articles more efficiently, facilitating evidence-based decision-making for patients with schizophrenia. SNA was used to determine the article subject categories associated with frequent citations. A combination of both publications and citations contributed to the IFWCD by highlighting the influential keywords on a thematic maps (Fig. ), as presented in previous articles. [ , – , , – ] There may be a reason for this result because physicians in schizophrenia must realize that T100SCIZ and related articles with higher CJAL scores are present in the current study. Of the common major topic keywords listed in these top-cited articles, the prefrontal cortex (301 = IFWCD) had the highest IFWCD, followed by schizophrenia (214.95) and 1st-episode psychosis (212.64) based on Equation 6. This may be partly attributed to the critical role of diagnosis revealed in T100SCHZs. This article applied SNA to describe the classification of T100SCHZs. With these classifications, physicians in schizophrenia may use the classification as an international communication tool to discuss any topic regarding schizophrenia. From our point of view, the classification approach can be applied to other scientific studies, not limited to schizophrenia. The Sankey diagram [ , , ] has been applied to bibliometric analysis in recent years. The Sankey is appropriate for the graph due to categorical dimensions instead of steps (or years) on the x-axis, nodes vertically on the y-axis, and edges correlated with the momentum between adjacent entities. Additionally, the reasons for demonstrating the CJAL score in this study are because the CJA score  takes 3 factors into account: the Category (C; e.g., review, original article, case report, etc), the journal “quality” (J; e.g., impact factor, JIF, or ranking of the journal), and the authorship order denoted by A); L-index  involving article citations. A publication final score is calculated by multiplying each of these 4 aspects (Equation 4). To date, no studies related to schizophrenia based on the CJAL score [ – ] have been identified in WoS. The current study on T100SCHZ is the first to use bibliometric analysis in the schizophrenia field. In Figure , a dashboard-type 4-quadrant radar plot provides overall information rather than those with many tables and graphs in traditional bibliometrics (e.g., 15 Tables and 27 Figures in a study  ). This is a unique and modern approach never seen before in the literature. Bibliometric analysis can be advanced in this manner in the future. 

The most-cited article in T100SCHZ was written by Correll et al (2017),  as marked by symbols A and B in Figure . This study was published in World Psychiatry, obtained 685 citations, and categorized as “schizophrenia.” This large-scale meta-analysis confirms that severe mental illness (SMI) patients have a significantly increased risk of cardiovascular disease (CVD) and CVD-related mortality and that elevated body mass indexes, the use of antipsychotics, and the screening and management of CVD require immediate clinical attention. The second most highly cited article was written by Huang et al (2019),  obtained 634 citations, and was published in Lancet Psychiatry, which was classified as “MENTAL-DISORDERS” in our study. According to this study, the prevalence of most mental disorders in China in 2013 was higher than that in 1982, 1993, and 2002 but lower than that in 2009. It is evident from this survey that there are serious challenges related to the high burden of disease identified, but it also offers valuable opportunities for policy makers and health-care professionals to investigate and address the factors that affect mental health in China. The third most-cited article appeared in Moreno et al (2020),  obtained 522 citations, was published in Lancet Psychiatry, and was classified as “SCHIZOPHRENIA” in our study. This article addresses that a group of clinicians, mental health experts, and users of mental health services have come together to discuss the challenges COVID-19 poses to mental health. In addition to providing the infrastructure to address previous system failings by disseminating good practices, the interconnectedness of the world makes society vulnerable to this infection. As a result, mental health care can be sustained, efficient, and equitable. Consequently, the COVID-19 pandemic may offer an opportunity to improve mental health care. 

This study has several noteworthy features. In the first instance, the CJAL score with decimal places can enhance the original h-index in terms of identifying the research accomplishments and rankings of a given group.  To measure the achievements of researchers and research institutions, we proposed using the CJAL score. The second feature is that the Sankey diagram was used to highlight a few vital entities and proved to be viable and feasible in bibliometrics. The third feature is the use of thematic (and collaborative) maps with a 4-quadrant radar plot and a scatter plot, providing readers with a representation of 4 perspectives in article entities with a glance view, particularly with research achievements denoted by the CJAL score instead of the Y-index in traditional studies  in a traditional manner. The scatter chart is a useful tool for predicting article citations using keyword weights, which might be applicable to future bibliometric analyses. In addition, the classification of subject categories (or themes defined in this study) using SNA is objective and unique when compared to previous studies using manual methods  or document types determined by PubMed.  The evidence suggests that the classification method is valid and worth recommending to future researchers. 

Further research should examine a number of issues. The first concern is that the software used to draw the Sankey (or thematic map) is not unique and irreplaceable. Several other software packages make it easy to draw the Sankey online. Second, dashboards in this study are displayed on Google Maps.  These installments are not free of charge because Google Maps requires a paid project key for using the cloud platform. Therefore, it is difficult for other authors to replicate the usage in a short period of time. Third, this study requires considerable computation to calculate the CJAL score. In the future, dedicated software will be required for development. Fourth, although the radar plot in this study was produced in R, the research achievements are determined by many other factors that should be considered when drawing the radar plot in the future. Fifth, only a few dimensions were selected in the Sankey diagram. Other important categories (e.g., journals, subject categories, and T100SCHZ) are required to display on the Sankey simultaneously. Future studies are recommended to involve more dimensions on the x-axis on the Sankey diagram. Finally, although T100SCHZs were extracted mainly from WoS, the results were different in articles retrieved from other databases (e.g., Google Scholar, Scopus, and PubMed). Future studies are required to extract T100SCHZ from more bibliometric databases. 

Through the examination of network characteristics in T100SCHZ, we achieved significant insights using various visualizations. The tool “Keywords Plus” can be employed to categorize article subjects, which are then represented on a thematic map. Moreover, it can forecast T100SCHZ citations, illustrated using scatter plots. Future research can expand the use of these visual tools to analyze bibliometric data across the top 100 cited articles in various fields, moving beyond the exclusive focus on schizophrenia as was the case in this study. 

We thank Enago ( www.enago.tw ) for the English language review of this manuscript. 

Data curation: Fong-Lin Jang. Investigation: Willy Chou. Methodology: Tsair-Wei Chien. 

 

