Assessment of Research Topic Prevalence by Journal Impact Quartile in Oral Health Sciences Using Bayesian Methods

Abstract

The relationship between research topics and academic prestige of journals is of relevance to assess venues for current research as well as trending areas of new research. This is of special relevance for those developing a research agenda or with defined productivity outcome expectations. This manuscript extracts prevalent topics using titles and abstracts from more than 10,000 manuscripts, constituting all published research in International Scientific Indexing (ISI) journals within the oral health specialties of oral surgery, orthodontics, and periodontics during 2018. Journals are clustered across four quartile categories according to their impact factors. The novelty of our work includes (a) an examination of a neglected unit of analysis (bigram) in oral health sciences which is of higher relevance than single-word topic definitions and (b) the use of an efficient Bayesian hierarchical approach to extract and rank topics across quartiles with information borrowing. Some topics persisted across quartile groups, while others show higher prevalence in specific quartiles, indicating that topics may find some journal quartiles a more appropriate venue for publication. All quartile groups show a prevalence of empirical research. The approach described in this manuscript offers the possibility to adjust/generate research agendas based on research topic prevalence and dynamics. This methodology is relevant for researchers looking to define their research agendas with potential outcomes aligned with the expectations of quantity and quartile set by their home institutions. It also serves researchers to assess most likely quartiles for publication of their work.

Keywords

oral surgery orthodontics periodontics text analysis MNIR research topics

Introduction

Dentistry encompasses multiple clinical specialties. These can be surgical (oral surgery and periodontics) or non-surgical (orthodontics), with novel research topics constantly stemming from them. Quantity and quality of research productivity have become a mainstream expectation across increasing numbers of institutions, further spreading the “publish or perish” concept in academia (McGrail et al., 2006; McKiernan et al., 2019). Knowledge of current trends in research topics becomes relevant to support the definition of research agendas that meet productivity expectations of the researcher’s home institution. While expectations are institution-based, research agendas must meet globally defined, peer-review standards of validity and relevance.

The academic literature is a key distribution channel for new research findings and hypotheses. As scientific venues for publication continue to grow, the number of published manuscripts across disciplines becomes larger by the millions each year (Jinha, 2010). Obtaining relevant scientific information from reputable sources and identifying connections between different scientific topics have become academic challenges (Pletscher-Frankild et al., 2015). Text analysis of discipline-specific relevant topics in specialized journals requires novel statistical tools that can adapt to the big data nature of the problem.

Text mining tools have been successful across multiple disciplines, including computer vision fields (Fei-Fei & Perona, 2005; Luo et al., 2015), statistical sciences (Butt et al., 2021), and social networks (Jiang et al., 2015). Within the health disciplines, the study of cancer (Zhu et al., 2013) or gene-disease associations (Pletscher-Frankild et al., 2015) are among the increasing areas of application where text analysis has been extensively utilized.

One developing area of text analysis is quotation-based bibliometric analysis, which provides information regarding the predominant areas within a research field (Park et al., 2017). The academic interest in scientific content analysis dates at least back to 1987. Eugene Garfield, a pioneer in the field of Scientometry, cataloged the classic references of studies cited in more than 100 scientific articles published in the Journal of the American Medical Association (Garfield, 1987). Since then, this type of analysis has been used in many dentistry disciplines such as dental education (Ullah, Adnan, & Afzal, 2019), oral health (Ullah, Zafar, et al., 2019), dental caries (Gansky, 2003; Workie & Belay, 2019), and orthodontics (Tarazona et al., 2018). These studies are mostly descriptive in nature, focusing on enumeration of terms in highly cited manuscripts or journals. They oftentimes ignore the differing relevance of topics based on their venue of publication, an issue that is relevant for researchers who may not be able to aim at highest impact journals, but still need to define a competitive research agenda. Also looking at each journal or group of journals independently ignores cross-information (information borrowing) that can enhance the analysis. Citation-based analyses, on the contrary, are biased against novel yet prevalent topics, which may not have been in the literature long enough to achieve high citation numbers. For example, the medical literature on Covid-19 was non-existent prior to 2020, and highly prevalent in the highest impact (medical and otherwise) journals since then, but citations of those works remained low during early 2020. Yet very impactful articles published around that topic in the early stages were hosted in top journals.

In recent years, many academic tools have emerged, all with different academic purposes, such as the altimeter analysis, which is complemented by bibliometrics and provides a better view of the impact of a research topic (Melero, 2015). The automatic literature analysis, and its integration with biomedical data resources, has reached new levels of sophistication with artificial intelligence tools (Feng et al., 2019; Rebholz-Schuhmann et al., 2012). Bayesian methods introduce new tools for information borrowing across clusters (Taddy, 2013b), which is more relevant to jointly assess topic frequency and relevance. This manuscript builds on the methodology of the latter reference and explores current trends within the oral health sciences literature across its disciplines. Some of the tools used for bibliographic analysis have morphed into pay-per-use products, such as inCites (Clarivite, 2021). This has increased the access gap for younger researchers in developing countries. While researchers in wealthier countries/institutions may have access to these tools to shape and gear their research agendas, others in developing countries face an informational gap when defining their areas of research focus. This manuscript provides a novel, freely available, self-contained approach, allowing for both information-borrowing and non-citation dependent analysis of topic prevalence within each discipline and provides younger researchers in low-income countries with a tool to extract relevant information to develop and support their research agendas. Self-containment is key for usability beyond the study period of any manuscript and to help address the issue of equity of access to information (American Library Association, 2021).

Simple word counts per category (e.g., journal quartile) in a descriptive fashion, as is common in the existing literature, assumes independence among the different research categories (e.g., journal quartiles). A more promising approach builds on text-specific dimension reduction methods, which are based on the multinomial form and exchangeability of token counts (e.g., words or combinations of words forming a topic) to borrow information efficiently among categories (Taddy, 2013b). A topic model treats document contents as drawn from multinomial distributions with topic probabilities arising as weighted combinations of “topic” factors. These probabilities are further modeled through a hierarchical approach, which allows for information borrowing among quartiles, thus extracting and utilizing for inference the dependence structure among them. We build on this approach to construct a quartile-based representation of the current state of the oral health literature, and to provide a framework for future analyses.

This manuscript has a dual contribution to the oral health sciences literature, which has focused historically on enumeration or identification of most-cited articles: (a) It provides a novel approach to study multi-word topic extraction beyond keyword analysis, which is demonstrated with a bigram example (two word combination), and (b) it builds on the Bayesian text analysis literature to extract information across journals with different impact characteristics (quartiles) and provides a ranking of topics with information borrowing across quartiles.

Knowledge of quartile-specific topic distributions can provide researchers in the oral health sciences and its disciplines with a tool to both statically and, when performed over time, dynamically assess the (relative) prevalence of topics across quartiles. This assessment can be relevant to avoid mis-alignment between researchers’ agendas and research productivity expectations within their home or prospective institutions. It can also serve doctoral students exploring a research career to assess whether their preferred research topics align with existing and future trends.

Method

Data

This study identifies the topics of highest prevalence in the oral health sciences’ academic literature during the year 2018. Results are provided across the disciplines of oral surgery, orthodontics, and periodontics. The study covers abstracts and titles of 1,876, 476, and 8,024 manuscripts published in specialized journals across those three specialties, respectively. The body of the manuscripts could have also been used. However, they would offer more diluted versions of the information content in their respective abstracts. They also contain noise from concepts that are less relevant to the key themes outlined in abstracts, such as statistical terminology in the methods/results sections or benchmarked methods in the introduction/discussion sections. Hence, we restrict our analysis to titles and abstracts, as recommended in Taddy (2013a).

Keyword sections of manuscripts, alone, can also misrepresent the relevance of a topic. Journals allow for a limited number of keywords, which can reduce or alter the real representation of specific topics. By looking at the title and abstract, an enhanced set of words can be studied. This provides a larger representation of the topics within the manuscripts.

Oral health manuscripts published in non-specialized journals, such as general health/medical journals, were excluded from the analysis. Inclusion of manuscripts from such journals would introduce noise, as topics unrelated to oral health sciences could appear as prevalent. The scope of journals is further restricted to those indexed by the International Scientific Indexing (ISI), which has been shown to provide a valid set of journals for study of academic specialties (Taddy, 2013a). A limited scope of the journal set is necessary to avoid inclusion of journals of questionable reputation or impact, which would not be considered acceptable choices by some researchers’ institutions. In addition, ISI provides a tool for quartile-assignment, which is of relevance when mapping articles to discipline penetration.

The study is further constrained to include only those journals which publish articles written in English, as topic mapping across languages would be subject to substantial risks of missmapping. This is further necessary because a dictionary is not defined (or necessary) under our approach, and mapping concepts across languages would require constantly updated dictionaries (and mapping tools) which would not be readily available for most researchers. Furthermore, most high–impact factor journals in the oral health sciences are written in English.

Model

A Bayesian multinomial inverse regression method (MNIR, Taddy, 2013b) was implemented to extract common topics in the form of bigrams. A bigram is a combination of two words that uniquely defines a topic of interest. The choice of bigram as the unit of analysis was arbitrary and solely to demonstrate the methodology. The methods utilized in this manuscript allow for any number of words (n-gram) to be extracted.

Abstracts (and titles) are considered exchangeable sets of tokens (which can be defined as uni-grams), or combinations of tokens (n-grams; Jurafsky & Martin, 2008). When dealing with text documents, tokens can be understood as regular stemmed words, where, for example, the words “periodontological,” “periodontology,” and “periodontal” are all mapped to a common stem “periodont.” This mapping is performed to reduce the risk of splitting equivalent topics into the multiple possible forms in which they can be expressed, with each such form ranking lower in prevalence than the combined form would rank. Lemmatization would require an up-to-date dictionary, as concepts may evolve and lemmas may develop or change over time. The proposed approach, which only uses stemming, does not require a dictionary to implement. Stop words are removed prior to stemming, following the aforementioned approach.

Following the notation in Taddy (2013b) and Casarin et al. (2019), $x_{i} = (x_{i 1}, \dots, x_{i p})^{'}$ is the vector of counts for $p$ possible bigrams in each document $i$ (combination of title and abstract). The empirical frequencies can be defined as $f_{i} = x_{i} / m_{i}$ , where $m_{i} = \sum_{j = 1}^{p} x_{i j}$ . Each of the possible $i = 1, \dots, n$ documents are linked to a sentiment variable $y_{i}$ . This sentiment variable represents the quartile of the journal where the manuscript was published, and consists of four ordered categories.

Modeling the conditional distribution of $y_{i} | x_{i}$ can be computationally taxing. Taddy (2013b) proposes to collapse token counts as $x_{y} = Σ_{i : y_{i} = y} x_{i}$ for every sentiment category $y \in Y$ , leading to

\begin{array}{l} x_{y} \sim M N [q_{y}, m_{y}] with \\ q_{y j} = \frac{\exp (α_{j} + y ψ_{j})}{\sum_{l = 1}^{p} \exp (α_{l} + y ψ_{l})} for j = 1, \dots, p, y \in Y \end{array}

(1)

where $x_{y}$ is a $p$ -dimensional multinomial distribution with size parameter vector $m_{y} = Σ_{i : y_{i} = y} m_{i}$ and probabilities $q_{y} = [q_{y 1}, \dots, q_{y p}]^{'}$ .

A latent Sufficient Reduction Score $z_{i} = ψ f_{i}$ is defined, and the model is completed with independent gamma-Laplace non-informative priors. These priors allow for efficient calculation of the posterior mode through a simple optimization, avoiding traditional Markov Chain Monte Carlo approaches. This combines the efficiency of optimization with the interpretability of the Bayesian paradigm.

One additional advantage of the method is its tractability. As the information content is effectively added, the computational time increases linearly with the number of documents explored. This makes the technique scalable as the literature in the oral health sciences expands. For more details regarding the algorithm, which is freely available, and implementation of MNIR in R software, see Taddy (2013b).

Results

Bigrams were extracted and analyzed across manuscripts, journal quartiles, and specialty areas (oral surgery, orthodontics, and periodontics). The top 40 ranked topics are listed by specialty and journal quartile in Tables 1 and 2 (oral surgery), Tables 3 and 4 (orthodontics), and Tables 5 and 6 (periodontics). This is an arbitrary number of the highest ranked topics selected to fit the tables, although a significantly larger number of topics is available from the algorithm’s outcome. This range of topics of high prevalence was deemed sufficiently broad to provide readers with an overall picture of the most significant topics within each of the disciplines.

Table 1.

Forty Most Prevalent Research Topics in Oral Surgery Among Journals in The Top Two Quartiles.

Rank	Q1	Q2
1	oral maxillofacial	oral maxillofacial
2	maxillofacial surgeons	maxillofacial surgeons
3	soft tissue	significant differences
4	bone loss	control group
5	implant placement	significant difference
6	dental implants	head neck
7	craniomaxillofacial surgery	soft tissue l
8	prosthetic dentistry	computed tomography
9	implants placed	bone loss
10	marginal bone	implant placement
11	cleft lip	dental implants
12	bone formation	squamous cell
13	orthognathic surgery	cell carcinoma
14	temporomandibular joint	craniomaxillofacial surgery
15	dental implant	prosthetic dentistry
16	cleft palate	implants placed
17	oral cavity	marginal bone
18	alveolar bone	cleft lip
19	implant survival	bone formation
20	bone regeneration	orthognathic surgery
21	maxillary sinus	temporomandibular joint
22	third molar	dental implant
23	bone graft	oral cavity
24	class iii	new bone
25	class ii	alveolar bone
26	clinical radiographic	implant survival
27	periimplant bone	bone regeneration
28	implant stability	maxillary sinus
29	maxillofacial surgery	oral squamous
30	le fort	third molar
31	third molars	lip palate
32	clinical dentistry	cleft palate
33	bone level	bone graft
34	oral cancer	class iii
35	orthodontic treatment	class ii
36	sinus floor	clinical radiographic
37	tooth extraction	periimplant bone
38	surgeons aim	implant stability
39	electron microscopy	maxillofacial surgery
40	first molar	le fort

Table 2.

Forty Most Prevalent Research Topics in Oral Surgery Among Journals in the Bottom Two Quartiles.

Rank	Q3	Q4
1	oral maxillofacial	oral maxillofacial
2	maxillofacial surgeons	maxillofacial surgeons
3	control group	soft tissue
4	head neck	bone loss
5	soft tissue	implant placement
6	bone loss	dental implants
7	implant placement	squamous cell
8	dental implants	cell carcinoma
9	squamous cell	craniomaxillofacial surgery
10	cell carcinoma	prosthetic dentistry
11	craniomaxillofacial surgery	implants placed
12	prosthetic dentistry	marginal bone
13	implants placed	cleft lip
14	marginal bone	bone formation
15	cleft lip	orthognathic surgery
16	bone formation	temporomandibular joint
17	orthognathic surgery	dental implant
18	temporomandibular joint	oral cavity
19	dental implant	alveolar bone
20	oral cavity	cone beam
21	new bone	implant survival
22	alveolar bone	bone regeneration
23	cone beam	maxillary sinus
24	implant survival	oral squamous
25	bone regeneration	lip palate
26	maxillary sinus	bone graft
27	oral squamous	class iii
28	third molar	class ii
29	lip palate	mandibular canal
30	bone graft	clinical radiographic
31	class iii	periimplant bone
32	class ii	implant stability
33	clinical radiographic	maxillofacial surgery
34	case report	le fort
35	report describes	third molars
36	periimplant bone	clinical dentistry
37	prosthodontic society	neck cancer
38	implant stability	bone level
39	materials total	oral cancer
40	maxillofacial surgery	orthodontic treatment

Table 3.

Forty Most Prevalent Research Topics in Orthodontics Among Journals in the Top Two Quartiles.

Rank	Q1	Q2
1	orthodontic treatment	orthodontic treatment
2	class ii	class ii
3	class iii	class iii
4	tooth movement	tooth movement
5	computed tomography	computed tomography
6	bond strength	clinical dentistry
7	clinical dentistry	conebeam computed
8	conebeam computed	alveolar bone
9	alveolar bone	skeletal class
10	angle education	root resorption
11	skeletal class	soft tissue
12	root resorption	first molar
13	soft tissue	maxillary mandibular
14	first molar	anterior teeth
15	maxillary mandibular	first molars
16	patients treated	maxillary expansion
17	anterior teeth	lateral cephalograms
18	first molars	open bite
19	maxillary expansion	second molar
20	lateral cephalograms	iii malocclusion
21	open bite	central incisors
22	second molar	orthodontic tooth
23	mandibular incisors	orthognathic surgery
24	iii malocclusion	fixed appliances
25	central incisors	ii malocclusion
26	orthodontic tooth	cleft lip
27	orthognathic surgery	maxillary central
28	fixed appliances	maxillary first
29	ii malocclusion	root canal
30	cleft lip	bone thickness
31	maxillary central	lip palate
32	objective evaluate	orthodontic patients
33	maxillary first	mandibular plane
34	root canal	orthodontists objective
35	bone thickness	dental casts
36	lip palate	first premolar
37	significantly lower	rapid maxillary
38	orthodontic patients	central incisor
39	mandibular plane	dental arch
40	orthodontists objective	facial asymmetry

Table 4.

Forty Most Prevalent Research Topics in Orthodontics Among Journals in the Bottom Two Quartiles.

Rank	Q3	Q4
1	orthodontic treatment	orthodontic treatment
2	class ii	class ii
3	class iii	class iii
4	tooth movement	tooth movement
5	computed tomography	computed tomography
6	alveolar bone	conebeam computed
7	skeletal class	alveolar bone
8	root resorption	skeletal class
9	soft tissue	root resorption
10	first molar	soft tissue
11	maxillary mandibular	first molar
12	patients treated	maxillary mandibular
13	anterior teeth	anterior teeth
14	first molars	first molars
15	maxillary expansion	maxillary expansion
16	lateral cephalograms	lateral cephalograms
17	open bite	open bite
18	second molar	second molar
19	iii malocclusion	iii malocclusion
20	central incisors	central incisors
21	orthodontic tooth	orthodontic tooth
22	orthognathic surgery	orthognathic surgery
23	fixed appliances	fixed appliances
24	ii malocclusion	cleft lip
25	cleft lip	maxillary central
26	maxillary first	maxillary first
27	root canal	root canal
28	bone thickness	airway dimensions
29	lip palate	bone thickness
30	orthodontic patients	lip palate
31	mandibular plane	orthodontic patients
32	orthodontists objective	mandibular plane
33	dental casts	orthodontists objective
34	first premolar	dental casts
35	rapid maxillary	tooth color
36	central incisor	first premolar
37	treatment time	quality life
38	dental arch	rapid maxillary
39	facial asymmetry	resorption area
40	cortical bone	dental development

Table 5.

Forty Most Prevalent Research Topics in Periodontics Among Journals in the Top Two Quartiles.

Rank	Q1	Q2
1	periodont diseas	periodont diseas
2	chronic periodont	chronic periodont
3	bone loss	bone loss
4	alveolar bone	alveolar bone
5	soft tissu	clinic dentistri
6	clinic attach	soft tissu
7	attach level	clinic attach
8	dental implant	attach level
9	squamou cell	dental implant
10	cell carcinoma	squamou cell
11	implant placement	cell carcinoma
12	connect tissu	implant placement
13	periodont therapi	connect tissu
14	crevicular fluid	periodont therapi
15	periodont tissu	crevicular fluid
16	sever periodont	periodont tissu
17	gingiv recess	gingiv recess
18	bleed probe	pocket depth
19	tooth loss	bleed probe
20	periodont treatment	tooth loss
21	gingiv crevicular	periodont treatment
22	plaqu index	gingiv crevicular
23	oral health	plaqu index
24	porphyromona gingivali	oral health
25	stem cell	porphyromona gingivali l
26	oral squamou	stem cell
27	implant place	oral squamou
28	oral hygien	implant place
29	periodont healthi	oral hygien
30	clinic radiograph	periodont healthi
31	gingiv tissu	clinic radiograph
32	periodont ligament	gingiv tissu
33	western blot	periodont ligament
34	periodont paramet	western blot
35	patient chronic	periodont paramet
36	class ii	class ii
37	oral caviti	oral caviti
38	gene express	growth factor
39	attach loss	attach loss
40	periodont patient	root canal

Table 6.

Forty Most Prevalent Research Topics in Periodontics Among Journals in the Bottom Two Quartiles.

Rank	Q3	Q4
1	periodont diseas	chronic periodont
2	chronic periodont	bone loss
3	bone loss	alveolar bone
4	alveolar bone	soft tissu
5	soft tissu	clinic attach
6	clinic attach	growth factor
7	attach level	attach level
8	dental implant	dental implant
9	squamou cell	squamou cell
10	cell carcinoma	cell carcinoma
11	implant placement	implant placement
12	connect tissu	crevicular fluid
13	periodont therapi	periodont tissu
14	crevicular fluid	sever periodont
15	periodont tissu	gingiv recess
16	sever periodont	pocket depth
17	gingiv recess	bleed probe
18	pocket depth	show signific
19	leed probe	tooth loss
20	tooth loss	periodont treatment
21	growth factor	gingiv crevicular
22	periodont treatment	plaqu index
23	gingiv crevicular	oral health
24	plaqu index	porphyromona gingivali
25	oral health	stem cell
26	porphyromona gingivali	cell death
27	stem cell	oral squamou
28	oral squamou	implant place
29	implant place	oral hygien
30	oral hygien	periodont healthi
31	periodont healthi	clinic radiograph
32	clinic radiograph	gingiv tissu
33	gingiv tissu	periodont ligament
34	periodont ligament	periodont paramet
35	western blot	probe pocket
36	periodont paramet	patient chronic
37	probe pocket	class ii
38	class ii	oral caviti
39	patient periodont	attach loss
40	healthi control	root canal

Oral Surgery

Most bigrams were prevalent among all journal quartile categories, with relative rankings within each quartile also relatively stable across most topics. This indicates that, in most circumstances, the quality of the manuscript will likely be the factor determining the journal quartile of publication, rather than current prevalence of the topic in the literature. For example, oral maxillofacial and maxillofacial surgeons were the most frequent bigrams across all journal quartiles.

There are some cases, however, where bigrams were more prevalent in specific journal quartiles. Bigrams such as cleft palate were more prevalent among the top two quartiles, indicating that they are topics where researchers succeeded more at publishing in higher quartile journals. This may also be a reflection of the complexity to produce novel results in those areas of study. The low number of manuscripts addressing those topics in lower journal quartiles is also an indication that researchers were successful at publishing them in higher impact journals. In contrast, bigrams such as cone beam were more prevalent in lower quartile journals, indicating the opposite effect. Researchers pursuing such topics were mostly successful in publishing their manuscripts in lower quartile journals, potentially making it harder for them to meet institutional expectations of publication in higher quartile journals.

When looking at the overall trends, dental implant and soft tissue terms were prioritized over temporomandibular joint, facial trauma, craniomaxillofacial surgery, orthognathic surgery, and oral cancer issues.

Orthodontics

Prevalence of trends was also similar across quartiles among specialized orthodontics journals. Bigrams like orthodontic treatment, class ii, and class iii had sustained interest across quartiles. The same applies for computed tomography, a topic of great importance for diagnosis based on volumetric three-dimensional imaging, also associated with the emerging virtual planning technology. The converse was found in the lateral cephalograms bigram, which ranks lower.

A prevalent bigram was cleft lip, which was present across quartiles and represents a constant topic in research involving patients with growth and development alterations. This topic poses a clinical management challenge for orthodontic specialists, positioning it among the most prevalent topics of 2018.

Topics such as dental casts did not make it to the top 40 in the highest journal quartile, but they were more prevalent in the lower quartiles. On the contrary, topics such as bond strength were highly prevalent in the top quartile journals (ranked 6th), while almost negligible among other quartiles. This, again, demonstrates that some topics find their homes more easily within journals in specific quartiles.

Periodontics

Similar to the other specialties, most topics remained prevalent across journal quartiles, while others were more prevalent in specific quartiles. As an example, the gene express bigram is a topic that only appeared to be prevalent in top quartile journals. Similarly, topics related to western blot only appeared among the most ranked topics for the top three journal quartiles.

The most prevalent topics in periodontics were those related to clinical periodontal pathology, which is reflected in the periodontal disease, chronic periodont, bone loss, and alveolar bone bigrams. Likewise, very persistent topics were dental implants and implant placements.

Discussion

The number of scientific articles published currently exceeds 114 million, with 2.5 million new publications added each year (Jinha, 2010) across disciplines, and new journals of varying reputability are also created. As those numbers grow, the complexity of their analysis also increases. This study explores research trends across oral health science disciplines using articles published in 2018. Prevalent topics are extracted, ranked, and listed using information borrowing across journal quartiles. This is a major difference with prior studies in the dentistry literature, which focus more exclusively on enumeration with no information borrowing across journal groups (or that focus on elicitation of the most-cited articles).

This list of topics can be dynamic and depends on emerging technological trends and the interests of different research groups leading the discipline in different parts of the world. While the results provided in this manuscript represent a static view of the oral health literature, a dynamic analysis is possible by performing this analysis at multiple time points or across rolling windows.

Previous bibliometric studies have used the number of citations to define the impact of a given topic. This method suffers from several issues: (a) Self-citations, which account for up to 7% of bibliometric measurements (Kulkarni et al., 2011), can inflate specific topics; (b) citation of a manuscript on a given topic does not automatically imply that the citing paper covers the same area of research; and (c) novel topics that recently appear in the literature will have fewer citations, as those are influenced by the age of the article.

The results outlined in this manuscript demonstrate the prevalence of some topics across journal quartiles for each of the disciplines analyzed within the oral health sciences. These can be considered neutral areas of research, where quality of publication, rather than topic, will likely be the most important defining factor for the hosting journal quartile. However, some emerging research topics across oral health sciences were more prevalent in top quartile journals, while researchers focused on other topics found it more challenging for their topics to be published in those journals. This information can guide emerging oral health researchers to align their research agenda with trends in their disciplines. As academics experience increased pressure to publish (both in quantity and journal quality of their publications), identifying research topics becomes an important factor toward defining professional success as a researcher. This is also relevant for new researchers throughout their doctoral programs (and their advisors) seeking objective ways to define their doctoral research topic. Such early decisions about choice of research topics can affect where their initial work is published, as well as their associated career opportunities.

Among the limitations of this study are the constraints set to define the scope of appropriate journals. Some manuscripts in the area of oral health sciences will be published in specialized journals, while others will appear in more general health/medical journals, which were not included. In addition, some manuscripts will be written in languages other than English; however, those were not included in the study. Finally, some journals will not be ISI indexed, which were also excluded from the study. While this exclusion criteria may represent a limitation of the manuscript (a limitation shared by a large portion of the bibliometric literature), the outcomes presented remain relevant. These outcomes reflect the current interests within a large set of well-respected, specialized globally considered journals in oral health sciences. In addition, these exclusions support the provision of a picture of discipline-specific topics only, limiting the influence of areas that may be only tangentially related.

Future studies can build on the work in this study by expanding the bigrams to larger word structures, as well as exploring the dynamics of the topics over time. All bibliographic studies are static representations in nature, providing either a historical picture or, at best, a snapshot of the present. While we also provide a snapshot in time (2018) within the manuscript and demonstrate the advantages within this novel methodology, the approach can be applied over time at any point (through rolling windows of analysis) to identify not only emerging research topics but also those stalling or fading, even if still highly prevalent in the literature. This can be relevant to matriculating graduate students (and their advisors) who may prefer to focus on emerging research topics (even if still of low penetration in the literature) rather than topics already showing signs of exhaustion. For example, Covid-19 manuscripts may be prevalent in the current literature (across sciences). However, a focus on this topic solely based on current snapshots of relevance may be a risky choice for students graduating in several years.

While outside of the scope of this manuscript, the study could also have been restricted to a particular geographical area (region, group of universities, etc.) or comparative group (peer or aspirational universities). This restriction could allow researchers and department administrators to define areas where research is lacking (and among which departmental hires may be needed) or areas of specialization and differentiation (for marketing/promoting the department).

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research and/or authorship of this article.

ORCID iDs

Laura H. Gunn

Enrique ter Horst

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