There were the following entities with the largest number of contributions (denoted by hTs) made to PEID in T100PEID: 2013(32.97) in years, the US(45.69) in countries, 1.surgery(54.28) in subject categories, Spine(Phila Pa 1976)(29.34) in journals, Journal Article(46.9) in document types, and PMID=24239490(3.39) in articles. A proverb goes that one look is worth a thousand words and quite a few . In Figure 6, we present the top ten entities with the highest hTs, and we were able to achieve our goal of displaying influential entities in T100PEID on the Alluvial diagram. Traditionally, more than six Tables or Figures are required to display influential entities with contributions to the scholarly field (or discipline, e.g., PEID). The second research goal was also achieved to evidence MeSH terms in the prediction power of the number of article citations (F =15.21; p <0 .001).
4.1 Additional Information
T100PEIDs from PubMed's database were categorized into six categories based on their characteristics. We used Alluvial diagrams and network analysis to determine the features and underlying relationships in T100PEIDs. Using these concise diagrams, spine surgeons may find relevant articles more efficiently, facilitating evidence-based decision-making for patients with PEID.
SNA was used to determine the article subject categories associated with frequent citations. A combination of both publications and citations contributed to the highest hT index for the subject category "surgery." There may be a reason for this result because spine surgeons have to realize that PEID-related articles with higher hTs are present in the domain of surgery research. Of the common major topic MeSHs listed in these top-cited articles, "diagnosis" had the highest IFs. This may be partly attributed to the critical role of diagnosis revealed in T100PEIDs. This article applied SNA to describe the classification of PEID-related articles. With these classifications for PEID, spine surgeons may use the classification as an international communication tool to discuss any topic regarding PEID. From our point of view, the classification approach can be applied to other scientific studies, not limited to PEID.
The Alluvial diagram has been applied to bibliometric analysis in two studies [21,53]. The Alluvialis appropriate for their graphs due to categorical dimensions instead of steps (or years) on the x-axis, referring to the definitions of Sankey diagrams .
Someone pointed out that while Sankey diagrams are better known, Alluvialplots are generally a good deal easier to generate . It is only valid because the data are simple enough as the software  to draw the Alluvialwithout taking the weights (e.g., citations and hT-indices) into account. As such, it is harder to draw the Alluvialthan the Sankey, particularly in three situations: (1) the weights are yielded by SNA and proportionally allocated to nodes and arcs; (2) the flows between dimensions are backward extracted from the SNA instead of forward to the step-by-step process in the Sankey; and (3) Sankey diagrams placing nodes more freely than on Alluvialplot that instead requires their nodes to be aligned and cannot be randomly placed. We have not seen software to take those situations into account for drawing the Alluvialas we provided the teaching material in Appendix 2.
Additionally, the reasons for demonstrating the hT-index in this study are because (1) the hT-index has an identical h-core with the h-index , (2) there is a strong association with the h-index, and (3) all publications and citations are taken into account to overcome the disadvantage associated with many bibliometric indices.
To date, no studies related to PEID have been identified in PubMed. The current study on T100PEID is the first to use bibliometric analysis in the PEID field. In Figure 4, a dashboard-type IBP provides information rather than the 100 articles listed across all papers in a study. This is a unique and modern approach never seen before in the literature. The IBP presents the T100PEID in a single view and provides more context than a single metric, such as a citation metric (or the h-index) in bibliometrics. Bibliometric analysis can be advanced in this manner.
4.2 Three most-cited articles
The most-cited article in T100PEID was written by Kreiner et al. This study was published in Spine J  in 2014 and categorized as "Medicine, General & Internal". In this article, the authors summarize (1) the techniques used by evidence-based medicine and provide the best available evidence to assist practitioners in the care of patients with symptomatic lumbar disc herniation with radiculopathy and (2) the complete guideline document for future research.
The second most highly cited article was written by Coric et al and published in 2011 in J Neurosurg Spine, which was classified as "surgery" in our study. This was a prospective, randomized US FDA Investigational Device Exemption (IDE) pivotal trial conducted at 21 centers across the US, finding that KineflexC (SpinalMotion, Inc.) was associated with a significantly higher success rate than fusions while maintaining motion at the index level. Even though there were significantly fewer Kineflex C patients with severe adjacent-level radiographic changes following the 2-year follow-up, these results indicate that Kineflex C CTDR is a viable alternative to anterior cervical discectomy and fusion (ACDF) in select patients with cervical radiculopathy.
The third most-cited article appeared in J Bone Joint Surg Am by Sasso et al. in 2011 and was classified as "surgery" in our study. This article addresses that the arthroplasty cohort continued to show significantly greater improvements in the Neck Disability Index, neck pain score, arm pain score, and Short Form-36 physical component score, as well as the primary outcome measure, overall success, at 48 months following surgery.
Thus, spine surgeons should pay special attention to PELD, LDH, PELD, PETD, and PEID [1-4].
4.2 Implications and Changes
This study has several noteworthy features. In the first instance, the hT-index 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 hT index.
The second feature is that Alluvial was used to highlight a few vital entities and proved to be viable and feasible in bibliometrics.
The third feature is the use of IBPs [44,45], providing authors with a brand-new representation of every academic article, particularly with research achievements denoted by the hT-index instead of the median percentile only shown to authors of core articles in Web of Science (WOS) [60,61].
We presented those entities with the highest hTs in the Alluvial diagram. As a consequence, more than six Tables or Figures are required to demonstrate the important entities that have contributed to the scholarly field (or discipline, e.g., PEID).
In addition, the classification of subject categories using SNA is objective and unique when compared to previous studies using manual methods  or document types determined by PubMed . Despite the fact that no difference was found in the citations between the subject categories (F = 0.813, p = 0.543), the evidence suggests that the classification method is valid and worth recommending to future researchers. Although the hT-index is more complex to compute than the h-index, the problem can be solved by a dedicated software program. The hT-index computation has been analyzed at the link, which provides readers with the programming codes for understanding how the hT-index is calculated within a second.
4.3 Limitations and suggestions
Further research should examine a number of issues. The first concern is that the software used to draw the Alluvial diagrams[37,38] is not unique and irreplaceable. Several other software packages [56, 63, 64] make it easy to draw the Alluvial(or Sankey) online. However, they do not meet the three requirements (i.e., weights derived from the arcs in SNA, flows between dimensions backward derived from the SNA, and nodes aligned and vertically aligned to the respective dimension on the x-axis) required in this study.
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, the hT-index calculated by adding up the weights in the Ferrers tableau (i.e., all the cited papers in the list) requires considerable computation. As a result of the improved hardware, the time-consuming task is now trivial and equivalent to the computation of other bibliometric indices using dedicated software.
Fourth, although the IBP in this study was produced online, the research achievements are determined by many other factors (e.g., the journal impact factor, JIF) that should be considered when drawing the IBP (e.g., using the JIF-based hT index to draw the IBP).
Fifth, only a few dimensions were selected in the Alluvial diagram. Other important categories (e.g., research institutes and influential authors in T100PEID) are required to display on the Alluvial diagram simultaneously. Future studies are recommended to involve more dimensions on the x-axis on the Alluvial diagram.
Finally, although T100PEIDs were extracted mainly from PubMed, the results were different in articles retrieved from other databases (e.g., Google Scholar, Scopus, and WOS). Future studies are required to extract T100PEID from more bibliometric databases.