Background: Spine trauma, vertebral metastases, and osteoporosis (SVO) can result in serious health problems. If the diagnosis of SVO is delayed, the prognosis may be deteriorated. The use of artificial intelligence (AI) is an essential method for minimizing the diagnostic errors associated with SVO. research achievements (RAs) of SVO on AI are required as a result of the greatest number of studies on AI solutions reported. The study aimed to: classify article themes using visualizations, illustrate the characteristics of SVO on AI recently, compare RAs of SVO on AI between entities (e.g., countries, institutes, departments, and authors), and determine whether the mean citations of keywords can be used to predict article citations. Methods: A total of 31 articles from SVO on AI (denoted by T31SVOAI) have been found in Web of Science since 2018. The dominant entities were analyzed using the CJAL score and the Y-index. Five visualizations were applied to report: the themes of T31SVOAI and their RAs in comparison for article entities and verification of the hypothesis that the mean citations of keywords can predict article citations, including: network diagrams, chord diagrams, dot plots, a Kano diagram, and radar plots. Results: There were five themes classified (osteoporosis, personalized medicine, fracture, deformity, and cervical spine) by a chord diagram. The dominant entities with the highest CJAL scores were the United States (22.05), the University of Pennsylvania (5.72), Radiology (6.12), and Nithin Kolanu (Australia) (9.88). The majority of articles were published in Bone, J. Bone Miner. Res., and Arch. Osteoporos., with an equal count (=3). There was a significant correlation between the number of article citations and the number of weighted keywords ( F = 392.05; P < .0001). Conclusion: A breakthrough was achieved by displaying the characteristics of T31SVOAI using the CJAL score, the Y-index, and the chord diagram. Weighted keywords can be used to predict article citations. The five visualizations employed in this study may be used in future bibliographical studies.

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Background: When a new disease such starts to spread, the commonly asked questions are how deadly is it? and how many people are likely to die of this outbreak? The World Health Organization (WHO) announced in a press conference on January 29, 2020 that the death rate of COVID-19 was 2% on the case fatality rate (CFR). It was underestimated assuming no lag days from symptom onset to deaths while many CFR formulas have been proposed, the estimation on Bays theorem is worthy of interpretation. Hence, it is hypothesized that the over-loaded burdens of treating patients and capacities to contain the outbreak (LSBHRS) may increase the CFR. Methods: We downloaded COVID-19 outbreak numbers from January 21 to February 14, 2020, in countries/regions on a daily basis from Github that contains information on confirmed cases in >30 Chinese locations and other countries/regions. The pros and cons were compared among the 5 formula of CFR, including [A] deaths/confirmed; [B] deaths/(deaths + recovered); [C] deaths/(cases x days ago); [D] Bayes estimation based on [A] and the outbreak (LSBHRS) in each country/region; and [E] Bayes estimation based on [C] deaths/(cases x days ago). The coefficients of variance (CV = the ratio of the standard deviation to the mean) were applied to measure the relative variability for each CFR. A dashboard was developed for daily display of the CFR across each region. Results: The Bayes based on (A)[D] has the lowest CV (=0.10) followed by the deaths/confirmed (=0.11) [A], deaths/(deaths + recoveries) (=0.42) [B], Bayes based on (C) (=0.49) [E], and deaths/(cases x days ago) (=0.59) [C]. All final CFRs will be equal using the formula (from, A to E). A dashboard was developed for the daily reporting of the CFR. The CFR (3.7%) greater than the prior CFR of 2.2% was evident in LSBHRS, increasing the CFR. A dashboard was created to present the CFRs on COVID-19. Conclusion: We suggest examining both trends of the Bayes based on both deaths/(cases 7 days ago) and deaths/confirmed cases as a reference to the final CFR. An app developed for displaying the provisional CFR with the 2 CFR trends can improve the underestimated CFR reported by WHO and media.

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Background: The h-index of a researcher refers to the maximum number h of his/her publications that has at least h citations via the concept of the square area. The x-index is determined by the maximum area of a rectangle under the curve to interpret authors’ individual research achievements (IRAs). However, the properties of both metrics have not been compared and discussed before. This study aimed to investigate whether both metrics of h- and x-index are suitable for evaluating IRAs in a short period of years. Methods: By searching the PubMed database (Pubmed.com), we used the keyword “ PLoS One ” (journal) and downloaded 50,000 articles published in 2015 and 2016. A total of 146,346 citations were listed in PubMed Central and 27,035 authors(with h-index ≥1) were divided into 3 parts. Correlation coefficients among metrics (ie, AIF, h, g, Ag, and x-index) were examined. The bootstrapping method used for estimating 95% confidence intervals was applied to compare differences in metrics among author groups. The most cited authors and topic burst were visualized by social network analysis. The most prominent countries/areas were highlighted by the x-index and displayed via choropleth maps. Results: Results demonstrated that, first, the h-index had the least relation to other metrics and failed to differentiate authors’ IRAs among groups, particularly in a short time period. Second, the top 3 highest x-index for countries were the United States, China, and the UK but with the productivity-oriented feature. Third, the most cited medical subject headings (ie, MeSH terms) were genome, metabolome, and microbiology, and the most cited author was Lori Newman (whose x-index = 13.52, and h = 2) from Switzerland with the article (PMID = 26646541) cited 291 times. The need for the x-index combined with a visual map for displaying authors’ IRAs was verified and recommended. Conclusions: We verified that the h-index failed to differentiate authors’ IRAs among author groups in a short time period. The x-index combined with the Kano map is recommended in research for a better understanding of the authors’ IRAs in other journals or disciplines, not just limited to the journal of PloS One as we did in this study.

Background: The US Centers for Disease Control and Prevention (CDC) regularly issues “travel health notices” that address disease outbreaks of novel coronavirus disease (COVID)-19 in destinations worldwide. The notices are classified into 3 levels based on the risk posed by the outbreak and what precautions should be in place to prevent spreading. What objectively observed criteria of these COVID-19 situations are required for classification and visualization? This study aimed to visualize the epidemic outbreak and the provisional case fatality rate (CFR) using the Rasch model and Bayes's theorem and developed an algorithm that classifies countries/regions into categories that are then shown on Google Maps. Methods: We downloaded daily COVID-19 outbreak numbers for countries/regions from the GitHub website, which contains information on confirmed cases in more than 30 Chinese locations and other countries/regions. The Rasch model was used to estimate the epidemic outbreak for each country/region using data from recent days. All responses were transformed by using the logarithm function. The Bayes's base CFRs were computed for each region. The geographic risk of transmission of the COVID-19 epidemic was thus determined using both magnitudes (i.e., Rasch scores and CFRs) for each country. Results: The top 7 countries were Iran, South Korea, Italy, Germany, Spain, China (Hubei), and France, with values of {4.53, 3.47, 3.18, 1.65, 1.34 1.13, 1.06} and {13.69%, 0.91%, 47.71%, 0.23%, 24.44%, 3.56%, and 16.22%} for the outbreak magnitudes and CFRs, respectively. The results were consistent with the US CDC travel advisories of warning level 3 in China, Iran, and most European countries and of level 2 in South Korea on March 16, 2020. Conclusion: We created an online algorithm that used the CFRs to display the geographic risks to understand COVID-19 transmission. The app was developed to display which countries had higher travel risks and aid with the understanding of the outbreak situation.

Background: A common concern in the literature is the comparison of the similarities and differences between research journals, as well as the types of research they publish. At present, there are no clear methodologies that can be applied to a given article of interest. When authors use an effective and efficient method to locate journals in similar fields, they benefit greatly. By using the forest plot and major medical subject headings (MeSH terms) of Spine (Phila Pa 1976) compared to Spine J, this study: displays relatively similar journals to the target journal online and identifies the effect of the similarity odds ratio of Spine (Phila Pa 1976) compared to Spine J.Methods: From the PubMed library, we downloaded 1000 of the most recent top 20 most similar articles related to Spine (Phila Pa 1976) and then plotted the clusters of related journals using social network analysis (SNA). The forest plot was used to compare the differences in MeSH terms for 2 journals (Spine (Phila Pa 1976) and Spine J) based on odds ratios. The heterogeneity of the data was evaluated using the Q statistic and the I-square (I 2 ) index.Results: This study shows that: the journals related to Spine (Phila Pa 1976) can easily be presented on a dashboard via Google Maps; 8 journal clusters were identified using SNA; the 3 most frequently searched MeSH terms are surgery, diagnostic imaging, and methods; and the odds ratios of MeSH terms only show significant differences with the keyword "surgery" between Spine (Phila Pa 1976) and Spine J with homogeneity at I 2 = 17.7% (P = .27). Conclusions:The SNA and forest plot provide a detailed overview of the inter-journal relationships and the target journal using MeSH terms. Based on the findings of this research, readers are provided with knowledge and concept diagrams that can be used in future submissions to related journals.