The intervention of artificial intelligence to improve the weaning outcomes of patients with mechanical ventilation: Practical applications in the medical intensive care unit and the COVID-19 intensive care unit: A retrospective study.

Patients admitted to intensive care units (ICU) and receiving mechanical ventilation (MV) may experience ventilator-associated adverse events and have prolonged ICU length of stay (LOS). We conducted a survey on adult patients in the medical ICU requiring MV. Utilizing big data and artificial intelligence (AI)/machine learning, we developed a predictive model to determine the optimal timing for weaning success, defined as no reintubation within 48 hours. An interdisciplinary team integrated AI into our MV weaning protocol. The study was divided into 2 parts. The first part compared outcomes before AI (May 1 to Nov 30, 2019) and after AI (May 1 to Nov 30, 2020) implementation in the medical ICU. The second part took place during the COVID-19 pandemic, where patients were divided into control (without AI assistance) and intervention (with AI assistance) groups from Aug 1, 2022, to Apr 30, 2023, and we compared their short-term outcomes. In the first part of the study, the intervention group (with AI, n = 1107) showed a shorter mean MV time (144.3 hours vs 158.7 hours, P = .077), ICU LOS (8.3 days vs 8.8 days, P = .194), and hospital LOS (22.2 days vs 25.7 days, P = .001) compared to the pre-intervention group (without AI, n = 1298). In the second part of the study, the intervention group (with AI, n = 88) exhibited a shorter mean MV time (244.2 hours vs 426.0 hours, P = .011), ICU LOS (11.0 days vs 18.7 days, P = .001), and hospital LOS (23.5 days vs 40.4 days, P < .001) compared to the control group (without AI, n = 43). The integration of AI into the weaning protocol led to improvements in the quality and outcomes of MV patients.

Effectiveness of abdominal sandbag training in enhancing diaphragm muscle function and exercise tolerance in patients with chronic respiratory failure.

BACKGROUND: Chronic respiratory failure is a common cause of ventilator dependence in the intensive care unit (ICU). The causes of chronic respiratory failure include primary disease or complications, such as ICU-acquired weakness. Traditional practice requires patients to remain immobile and bedridden; however, recent evidence suggests that early adequate exercise promotes recovery without increasing risks. In this study, we explored the efficacy of planned progressive abdominal sandbag training in promoting the successful withdrawal of patients with chronic respiratory failure from mechanical ventilation. METHODS: This study was conducted between April 2019 and November 2020. Patients were recruited and divided into two groups: abdominal sandbag training group and control group (no training). The training group participated in a 3-month daily pulmonary rehabilitation program, which involved a 30-min session of progressive sandbag loading on the upper abdomen as a form of diaphragmatic resistant exercise. The pressure support level of the ventilator was adjusted to maintain a tidal volume of 8 mL/kg. To investigate the effect of abdominal sandbag training on patients with chronic respiratory failure, we compared tidal volume, shallow breathing index, maximum respiratory pressure, and diaphragm characteristics between the training and control groups. RESULTS: This study included 31 patients; of them, 17 (54.8 %) received abdominal sandbag training and 14 (45.2 %) did not. No significant between-group difference was found in baseline characteristics. Compared with the control group, the training group exhibited considerable improvements in ventilation-related parameters (p < 0.001): the tidal volume markedly increased (p = 0.012), rapid shallow breathing index declined (p = 0.016), and maximum respiratory pressure increased (p < 0.001) in the training group. The diaphragm motion value (p = 0.048) and diaphragm thickness (p = 0.041) were greater in the training group than in the control group. Nine patients (52.9 %) in the training group were removed from the ventilator compared with 1 (7.1 %) in the control group (p = 0.008). CONCLUSIONS: Abdominal sandbag training may be beneficial for patients dependent on a ventilator. The training improves the function of the diaphragm muscle, thereby increasing tidal volume and reducing the respiratory rate and rapid shallow breathing index, thus facilitating withdrawal from ventilation. This training approach may also improve the thickness and motion of the diaphragm and the rate of ventilator detachment.

Identifying China's distinctive academic fields among the top 2% of influential scientists: A bibliometric analysis using Rasch KIDMAP.

BACKGROUND: Leading scientists worldwide are recognized by their placement in the top 2% based on their career-spanning contributions, as categorized by the Science-Metrix classification. However, there has been little focus on the unique scientific fields and subfields that separate countries. Although the KIDMAP in the Rasch model has been utilized to depict student performance, its application in identifying distinctive academic areas remains unexplored. Our study uses this model to pinpoint unique research domains specific to countries based on the top 2% author data. METHODS: We sourced our data from Elsevier career-long author database updated until the end of 2022. This encompassed 168 countries, 22 scientific domains, and 174 subdomains in 2021 and 2022 (with a total of 194,983 and 204,643 researchers, respectively). Our approach was threefold: identifying unique fields, subfields, and researchers. Visualizations included scatter plots, KIDMAP, and the Impact Bam Plot (IBP). China distinctive research areas were identified using the Rasch KIDMAP. RESULTS: Key insights include the following: The US prevailing dominance in scientific domains in both 2021 and 2022. China distinct contribution in the "Enabling & Strategic Technologies" domain. China notable emphasis on the "Complementary & Alternative Medicine" subfield in 2022. Dr Phillip Low from the Mayo Clinic (US) emerged as a leading figure in the General & Internal Medicine research domain. CONCLUSIONS: Despite trailing the US in global research achievements, China showcased pronounced expertise in specific scientific areas, such as the "Complementary & Alternative Medicine" subfield in 2022, when compared to China other subfields based on the level of academic performance (-3.09 logits). Future research could benefit from incorporating KIDMAP visuals to gauge other countries' strengths in various research sectors, expanding beyond the China-centric focus in this study.

Evaluating the dependability of reference-driven citation forecasts amid the COVID-19 pandemic: A bibliometric analysis across diverse journals.

BACKGROUND: The journal impact factor significantly influences research publishing and funding decisions. With the surge in research due to COVID-19, this study investigates whether references remain reliable citation predictors during this period. METHODS: Four multidisciplinary journals (PLoS One, Medicine [Baltimore], J. Formos. Med. Assoc., and Eur. J. Med. Res.) were analyzed using the Web of Science database for 2020 to 2022 publications. The study employed descriptive, predictive, and diagnostic analytics, with tools such as 4-quadrant radar plots, univariate regressions, and country-based collaborative maps via the follower-leading cluster algorithm. RESULTS: Six countries dominated the top 20 affiliations: China, Japan, South Korea, Taiwan, Germany, and Brazil. References remained strong citation indicators during the COVID-19 period, except for Eur. J. Med. Res. due to its smaller sample size (n = 492) than other counterparts (i.e., 41,181, 12,793, and 1464). Three journals showed higher network density coefficients, suggesting a potential foundation for reference-based citation predictions. CONCLUSION: Despite variations among journals, references effectively predict article citations during the COVID-19 era, underlining the importance of network density. Future studies should delve deeper into the correlation between network density and citation prediction.

Analyzing shifts in age-related macular degeneration research trends since 2014: A bibliometric study with triple-map Sankey diagrams (TMSD).

BACKGROUND: Age-related macular degeneration (AMD) is the primary cause of vision impairment in older adults, especially in developed countries. While many articles on AMD exist in the literature, none specifically delve into the trends based on document categories. While bibliometric studies typically use dual-map overlays to highlight new trends, these can become congested and unclear with standard formats (e.g., in CiteSpace software). In this study, we introduce a unique triple-map Sankey diagram (TMSD) to assess the evolution of AMD research. Our objective is to understand the nuances of AMD articles and show the effectiveness of TMSD in determining whether AMD research trends have shifted over the past decade. METHODS: We collected 7465 articles and review pieces related to AMD written by ophthalmologists from the Web of Science core collection, accumulating article metadata from 2014 onward. To delve into the characteristics of these AMD articles, we employed various visualization methods, with a special focus on TMSD to track research evolution. We adopted the descriptive, diagnostic, predictive, and prescriptive analytics (DDPP) model, complemented by the follower-leading clustering algorithm (FLCA) for clustering analysis. This synergistic approach proved efficient in identifying and showcasing research focal points and budding trends using network charts within the DDPP framework. RESULTS: Our findings indicate that: in countries, institutes, years, authors, and journals, the dominant entities were the United States, the University of Bonn in Germany, the year 2021, Dr Jae Hui Kim from South Korea, and the journal "Retina"; in accordance with the TMSD, AMD research trends have not changed significantly since 2014, as the top 4 categories for 3 citing, active, and cited articles have not changed, in sequence (Ophthalmology, Science & Technology - Other Topics, General & Internal Medicine, Pharmacology & Pharmacy). CONCLUSION: The introduced TMSD, which incorporates the FLCA algorithm and features in 3 columns-cited, active, and citing research categories-offers readers clearer insights into research developments compared to the traditional dual-map overlays from CiteSpace software. Such tools are especially valuable for streamlining the visualization of the intricate data often seen in bibliometric studies.

Evaluating cluster analysis techniques in ChatGPT versus R-language with visualizations of author collaborations and keyword cooccurrences on articles in the Journal of Medicine (Baltimore) 2023: Bibliometric analysis.

BACKGROUND: Analyses of author collaborations and keyword co-occurrences are frequently used in bibliographic research. However, no studies have introduced a straightforward yet effective approach, such as utilizing ChatGPT with Code Interpreter (ChatGPT_CI) or the R language, for creating cluster-oriented networks. This research aims to compare cluster analysis methods in ChatGPT_CI and R, visualize country-specific author collaborations, and then demonstrate the most effective approach. METHODS: The research focused on articles and review pieces from Medicine (Baltimore) published in 2023. By August 20, 2023, we had gathered metadata for 1976 articles using the Web of Science core collections. The efficiency and effectiveness of cluster displays between ChatGPT_CI and R were compared by evaluating their time consumption. The best method was then employed to present a series of visualizations of country-specific author collaborations, rooted in social network and cluster analyses. Visualization techniques incorporating network charts, chord diagrams, circle bar plots, circle packing plots, heat dendrograms, dendrograms, and word clouds were demonstrated. We further highlighted the research profiles of 2 prolific authors using timeline visuals. RESULTS: The research findings include that (1) the most active contributors were China, Nanjing Medical University (China), the Medical School Department, and Dr Chou from Taiwan when considering countries, institutions, departments, and individual authors, respectively; (2) the highest cited articles originated from Medicine (Baltimore) accounting for 4.53%: New England Journal of Medicine, PLOS ONE, LANCET, and The Journal of the American Medical Association, with respective contributions of 3.25%, 2.7%, 2.52%, and 1.54%; (3) visual cluster analysis in R proved to be more efficient and effective than ChatGPT_CI, reducing the time taken from 1 hour to just 3 minutes; (4) 7 cluster-focused networks were crafted using R on a custom platform; and (5) the research trajectories of 2 prominent authors (Dr Brin from the United States and Dr Chow from Taiwan) and articles themes in Medicine 2023 were depicted using timeline visuals. CONCLUSIONS: This research highlighted the efficient and effective methods for conducting cluster analyses of author collaborations using R. For future related studies, such as keyword co-occurrence analysis, R is recommended as a viable alternative for bibliographic research.

Exploring the top-cited literature in telerehabilitation for joint replacement using the descriptive, diagnostic, predictive, and prescriptive analytics model: A thematic and bibliometric analysis.

BACKGROUND: Telerehabilitation offers a novel approach supplementing or replacing traditional physical rehabilitation. While research on telerehabilitation for joint replacement (TJR) has expanded, no study has investigated the top 100 cited articles (T100TJR) using the descriptive, diagnostic, predictive, and prescriptive analytics (DDPP) model. This study aims to examine the features of T100TJR in TJR through the DDPP approaches. METHODS: A comprehensive search of the Web of Science Core Collection was conducted to locate all pertinent English-language documents from the database's inception until August 2, 2023. The T100TJR articles were then identified based on citation counts. The DDPP analytics model, along with 7 visualization techniques, was used to analyze metadata elements such as countries, institutions, journals, authors, references, and keywords. An impact timeline view was employed to highlight 2 particularly noteworthy articles. RESULTS: We analyzed 712 articles and observed a consistent upward trend in publications, culminating in a noticeable peak in 2022. The United States stood out as the primary contributor. A detailed examination of the top 100 articles (T100TJR) revealed the following leading contributors since 2010: the United States (by country), University of Sherbrooke, Canada (by institutions), 2017 (by publication year), and Dr Hawker from Canada (by authors). We delineated 4 major themes within these articles. The theme "replacement" dominated, featuring in 89% of them. There was a strong correlation between the citations an article garnered and its keyword prominence (F = 3030.37; P < .0001). Additionally, 2 particularly high-impact articles were underscored for recommendation. CONCLUSIONS: Telerehabilitation for TJR has seen rising interest, with the U.S. leading contributions. The study highlighted dominant themes, especially "replacement," in top-cited articles. The significant correlation between article citations and keyword importance indicates the criticality of keyword selection. The research underscores the importance of 2 pivotal articles, recommending them for deeper insights.

Trends and hotspots related to traditional and modern approaches on acupuncture for stroke: A bibliometric and visualization analysis.

BACKGROUND: Acupuncture role in stroke treatment and post-stroke rehabilitation has garnered significant attention. However, there is a noticeable gap in bibliometric studies on this topic. Additionally, the precision and comprehensive methodology of cluster analysis remain underexplored. This research sought to introduce an innovative cluster analysis technique (called follower-leading clustering algorithm, FLCA) to evaluate global publications and trends related to acupuncture for stroke in the recent decade. METHODS: Publications pertaining to acupuncture for stroke from 2013 to 2022 were sourced from the Web of Science Core Collection. For the assessment of publication attributes-including contributing countries/regions (e.g., US states, provinces, and major cities in China) in comparison to others, institutions, departments, authors, journals, and keywords-we employed bibliometric visualization tools combined with the FLCA algorithm. The analysis findings, inclusive of present research status, prospective trends, and 3 influential articles, were presented through bibliometrics with visualizations. RESULTS: We identified 1050 publications from 92 countries/regions. An initial gradual rise in publication numbers was observed until 2019, marking a pivotal juncture. Prominent contributors in research, based on criteria such as regions, institutions, departments, and authors, were Beijing (China), Beijing Univ Chinese Med (China), the Department of Rehabilitation Medicine, and Lidian Chen (Fujian). The journal "Evid.-based Complement Altern" emerged as the most productive. The FLCA algorithm was effectively employed for co-word and author collaboration analyses. Furthermore, we detail the prevailing research status, anticipated trends, and 3 standout articles via bibliometrics. CONCLUSION: Acupuncture for stroke presents a vast research avenue. It is imperative for scholars from various global regions and institutions to transcend academic boundaries to foster dialogue and cooperation. For forthcoming bibliometric investigations, the application of the FLCA algorithm for cluster analysis is advocated.

Using Rasch Wright map to identify hospital employee satisfaction during and before COVID-19.

During the surge of the COVID-19 outbreak, medical personnel attended to countless patients, which adversely affected their mental well-being. To support their staff, hospitals implemented guidelines that focused on promoting mental health among medical professionals. The hypothesis that employee satisfaction declined during the COVID-19 pandemic needs confirmation. Several findings were derived from a series of visualizations using Rasch Wright map. The research sample was taken from a medical center in southern Taiwan based on satisfaction survey data from 2017 to 2022 (n = 1222). Perceptions on job satisfaction perceptions during and prior to COVID-19 in 2 stages of 2017 to 2019 and 2020 to 2022 were compared using Rasch Wright map. Through a series of visualizations, including the dimension with the highest satisfaction, the demographical category of hospital employees with the lowest satisfaction during the pandemic, and Rasch Wright map displaying employs' perfections on 4 domains over years. The results indicated: Employee satisfaction was significantly lower during the COVID-19 period in 2 domains: compensation and benefits, work atmosphere; among the 23 questions, Question 5 (regarding meals provided by the hospital to staff) scored the lowest, while Question 23 (regarding the hospital emergency response and disaster prevention capabilities) scored the highest. Among the 4 domains, organizational leadership had the highest satisfaction; out of 104 demographic variables, 21 groups showed that employee satisfaction during the pandemic was significantly (P < .05) lower than before the pandemic; the selection of specific demographic variables is for top-tier supervisors, and they showed that employee satisfaction during the pandemic was significantly (P < .05) lower than before the pandemic across all 4 dimensions. Therefore, this study accepts the hypothesis that employee satisfaction was negatively affected during the COVID-19 period on 2 domains only: compensation and benefits, work atmosphere. The study visual examination, especially using Rasch Wright map, offers a comparative perspective on hospital staff satisfaction and serves as a methodological guide for subsequent satisfaction research.

Circle packing charts generated by ChatGPT to identify the characteristics of articles by anesthesiology authors in 2022: Bibliometric analysis.

BACKGROUND: The ChatGPT (Open AI, San Francisco, CA), denoted by the Chat Generative Pretrained Transformer, has been a hot topic for discussion over the past few months. A verification of whether the code for drawing circle packing charts (CPCs) with R can be generated by ChatGPT and used to identify characteristics of articles by anesthesiology authors is needed. This study aimed to provide insights into article characteristics in the field of anesthesiology and to highlight the potential of ChatGPT for data visualization techniques (e.g., CPCs) in bibliometric analysis. METHODS: A total of 23,012 articles were indexed in PubMed in 2022 by authors in the field of anesthesiology. The code for drawing CPCs with R was generated by ChatGPT and then modified by the authors to identify the characteristics of articles in 2 forms: 23,012 and 100 top-impact factors in journals (T100IF). Using CPCs and 3 other visualizations-network charts, impact beam plots, and Sankey diagrams-we were able to display article features commonly used in bibliometric analysis. The author-weighted scheme and absolute advantage coefficient were used to assess dominant entities, such as countries, institutes, authors, and themes (defined by PubMed and MeSH terms). RESULTS: Our findings indicate that: further modifications should be made to the code generated by ChatGPT for drawing CPCs in R; publications in the field of anesthesiology are dominated by China, followed by the United States and Japan; Capital Medical University (China) and Showa University Hospital (Japan) dominate research institutes in terms of publications and IF, respectively; and COVID-19 is the most frequently reported theme in T100IF, accounting for 29%. CONCLUSIONS: No such articles with CPCs regarding bibliometrics have ever been found in PubMed. The code for drawing CPCs with R can be generated by ChatGPT, but further modification is required for implementation in bibliometrics. CPCs should be used in future studies to identify the characteristics of articles in other areas of research rather than limiting them to anesthesiology, as we did in this study.

2023 TAMIS/TSOC/TACVPR Consensus Statement for Patients with Acute Myocardial Infarction Rehabilitation.

Cardiac rehabilitation is a comprehensive intervention recommended in international and Taiwanese guidelines for patients with acute myocardial infarction. Evidence supports that cardiac rehabilitation improves the health-related quality of life, enhances exercise capacity, reduces readmission rates, and promotes survival in patients with cardiovascular disease. The cardiac rehabilitation team is comprehensive and multidisciplinary. The inpatient, outpatient, and maintenance phases are included in cardiac rehabilitation. All patients admitted with acute myocardial infarction should be referred to the rehabilitation department as soon as clinically feasible. Pre-exercise evaluation, including exercise testing, helps physicians identify the risks of cardiac rehabilitation and organize appropriate exercise prescriptions. Therefore, the Taiwan Myocardial Infarction Society (TAMIS), Taiwan Society of Cardiology (TSOC), and Taiwan Academy of Cardiovascular and Pulmonary Rehabilitation (TACVPR) address this consensus statement to assist healthcare practitioners in performing cardiac rehabilitation in patients with acute myocardial infarction.

Assessing the impact of COVID-19 on outpatient and inpatient revenues: A comparative analysis of large and small hospitals in Taiwan.

BACKGROUND: The COVID-19 pandemic has had profound effects on healthcare systems worldwide, not only by straining medical resources but also by significantly impacting hospital revenues. These economic repercussions have varied across different hospital departments and facility sizes. This study posits that outpatient (OPD) revenues experienced greater reductions than inpatient (IPD) revenues and that the financial impact was more profound in larger hospitals than in smaller hospitals. METHODS: We collected data on patient case numbers and associated revenues for 468 hospitals from the Taiwan government-run National Health Insurance Administration website. We then employed Microsoft Excel to construct scatter plots using the trigonometric function (=DEGREES (Atan (growth rate))) for each hospital. Our analysis scrutinized 4 areas: the case numbers and the revenues (represented by medical fees) submitted to the Taiwan government-run National Health Insurance Administration in both March and April of 2019 and 2020 for OPD and IPD departments. The validity of our hypotheses was established through correlation coefficients (CCs) and chi-square tests. Moreover, to visualize and substantiate the hypothesis under study, we utilized the Kano diagram. A higher CC indicates consistent counts and revenues between 2019 and 2020. RESULTS: Our findings indicated a higher impact on OPDs, with CCs of 0.79 and 0.83, than on IPDs, which had CCs of 0.40 and 0.18. Across all hospital types, there was a consistent impact on OPDs (P = .14 and 0.46). However, a significant variance was observed in the impact on IPDs (P < .001), demonstrating that larger hospitals faced greater revenue losses than smaller facilities, especially in their inpatient departments. CONCLUSION: The two hypotheses confirmed that the COVID-19 pandemic impacted outpatient departments more than inpatient departments. Larger hospitals in Taiwan faced greater financial challenges, especially in inpatient sectors, underscoring the pandemic's varied economic effects. The COVID-19 pandemic disproportionately affected outpatient departments and larger hospitals in Taiwan. Policymakers must prioritize support for these areas to ensure healthcare resilience in future epidemics. The research approach used in this study can be utilized as a model for similar research in other countries affected by COVID-19.

Thematic maps with scatter and 4-quadrant plots in R to identity dominant entities on schizophrenia in psychiatry since 2017: Bibliometric analysis.

BACKGROUND: Schizophrenia affects more than 21 million people worldwide. There have been a number of articles published in the literature regarding schizophrenia. It is unclear which authors contributed the most to the field of schizophrenia. This study examines which article entities (affiliated countries, institutes, journals, and authors) earn the most research achievements (RAs) and whether keywords in articles are associated with the number of article citations. METHODS: As of August 25, 2022, 20,606 abstracts published on schizophrenia in psychiatry since 2017 were retrieved from the WoS core collection (WoSCC). RAs were measured using the category, JIF, authorship, and L-index (CJAL) score. The follower-leading cluster algorithm (FLCA) was used to examine clusters of keywords associated with core concepts of research. There were 7 types of visualizations used to report the study results, including Sankey diagrams, choropleth maps, scatter charts, radar plots, and cluster plots. A hypothesis was examined that the mean number of citations for keywords could predict the number of citations for 100 top-cited articles(T100SCHZ). RESULTS: The results indicate that 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, respectively. The journal of Schizophrenia Res had higher citations (19,017), counts (1681), and mean citations (11.31) in journals. There was a significant correlation between article citations and weighted keywords (F = 1471.74; P < .001). CONCLUSION: Seven visualizations were presented to report the study results, particularly with thematic maps using scatter and 4-quadrant plots produced in R programming language. We recommend that more future bibliographical studies utilize CAJL scores and thematic maps to report their findings, not restrict themselves solely to schizophrenia in psychiatry as done in this study.

A modern approach with follower-leading clustering algorithm for visualizing author collaborations and article themes in skin cancer research: A bibliometric analysis.

BACKGROUND: Skin cancers (SCs) arise due to the proliferation of atypical cells that have the potential to infiltrate or metastasize to different areas of the body. There is a lack of understanding regarding the country-based collaborations among authors (CBCA) and article themes on SCs. A clustering algorithm capable of categorizing CBCA and article themes on skin cancer is required. This study aimed to apply a follower-leading clustering algorithm to classify CBCA and article themes and present articles that deserve reading in recent ten years. METHODS: Between 2013 and 2022, a total of 6526 articles focusing on SC were extracted from the Web of Science core collection. The descriptive, diagnostic, predictive, and prescriptive analytics model was employed to visualize the study results. Various visualizations, including 4-quadrant radar plots, line charts, scatter plots, network charts, chord diagrams, and impact beam plots, were utilized. The category, journal, authorship, and L-index score were employed to assess individual research achievements. Diagnostic analytics were used to cluster the CBCA and identify common article themes. Keyword weights were utilized to predict article citations, and noteworthy articles were highlighted in prescriptive analytics based on the 100 most highly cited articles on SC (T100SC). RESULTS: The primary entities contributing to SC research include the United States, the University of California, San Francisco in US, dermatology department, and the author Andreas Stang from Germany, who possess higher category, journal, authorship, and L-index scores. The Journal of the American Academy of Dermatology has published the highest number of articles (n = 336, accounting for 5.16% of the total). From the T100SC, 7 distinct themes were identified, with melanoma being the predominant theme (92% representation). A strong correlation was observed between the number of article citations and the keyword weights (F = 81.63; P < .0001). Two articles with the highest citation counts were recommended for reading. CONCLUSION: By applying the descriptive, diagnostic, predictive, and prescriptive analytics model, 2 noteworthy articles were identified and highlighted on an impact beam plot. These articles are considered deserving of attention and could potentially inspire further research in the field of bibliometrics, focusing on relevant topics related to melanoma.

The executive functions among patients with an initial Glasgow coma scale score of 15.

Executive functions (EF) were the critical neuropsychological functions linked to long-term adaptation. Given the heterogeneous prognosis trajectories of mild traumatic brain jury (mTBI), the mildest TBI may not always be benign in the chronic stage. The present study explored the long-term EF in patients with chronic complicated mTBI and a Glasgow Coma Scale (GCS) score of 15. Fifty patients with complicated mTBI and GCS scores of 15 and 35 control participants were recruited in this study. Medical records were retrospectively analyzed, and neuropsychological assessments and subjective measures examined the neuropsychological functions. Compared with healthy controls, complicated mTBI patients with a GCS score of 15 performed significantly worse on most EF assessments, including longer reaction time (RT) and poor cognitive flexibility and abstract reasoning performances. Patients also reported more EF problems and lower quality of life (QoL) than healthy controls. Females and those with subdural hematoma (SDH) had significantly longer reaction times (RT) on executive attention tests. This study found that complicated mTBI with a GCS score of 15 had incomplete recovery of EF, even in the chronic stage. We suggest that early neuropsychological assessment and rehabilitation should be arranged for such patients.

Aspergillus coinfection in critically Ill patients with severe dengue.

In 2014-2015, a significant outbreak of dengue fever occurred in southern Taiwan, with a subsequent decline in dengue incidence. Despite this, there is emerging concern about virus-associated aspergillosis, yet limited research has explored coinfections involving dengue and aspergillosis. We conducted a retrospective study at a single center in Southern Taiwan, specifically focusing on dengue patients admitted to the intensive care unit during the period between July and November 2015. Among the 142 dengue patients studied, only 8.06 % (10/142) underwent serum galactomannan testing, with a single patient undergoing bronchoalveolar lavage (BAL) galactomannan assay. Out of those tested, 20 % (2/10) returned positive serum galactomannan results. Herein, we present two consecutive cases of coinfection involving dengue and pulmonary aspergillosis in immunocompetent patients.

Differences in productivity and collaboration patterns on spine-related research between neurosurgeons and orthopedic spine surgeons: Bibliometric analysis.

BACKGROUND: Spinal surgeries are commonly performed by neurosurgeons and orthopedic spine surgeons, with many spine-related articles published by them. However, there has been limited research that directly compares their research achievements. This article conducted a comparative analysis of spine-related research achievements between neurosurgeons and orthopedic spine surgeons. This study examines differences in productivity and impact on spine-related research between them using these measures, particularly with a novel clustering algorithm. METHODS: We gathered 2148 articles written by neurosurgeons and orthopedic spine surgeons from the Web of Science core collections, covering the period from 2013 to 2022. To analyze author collaborations, we employed the follower-leader clustering algorithm (FLCA) and conducted cluster analysis. A 3-part analysis was carried out: cluster analysis of author collaborations; mean citation analysis; and a category, journal, authorship, L-index (CJAL) score based on article category, journal impact factors, authorships, and L-indices. We then utilized R to create visual displays of our findings, including circle bar charts, heatmaps with dendrograms, 4-quadrant radar plots, and forest plots. The mean citations and CJAL scores were compared between neurosurgeons and orthopedic spine surgeons. RESULTS: When considering first and corresponding authors, orthopedics authors wrote a greater proportion of the articles in the article collections, accounting for 75% (1600 out of 2148). The CJAL score based on the top 10 units each also favored orthopedic spine surgeons, with 71% (3626 out of 6139) of the total score attributed to them. Using the FLCA, we observed that orthopedic spine surgeons tended to have more collaborations across countries. Additionally, while citation per article favored orthopedic spine surgeons with standard mean difference (= -0.66) and 95%CI: -0.76, -0.56, the mean CJAL score in difference (= 0.34) favored neurosurgeons with 95%CI: 0.24 0.44. CONCLUSION: Orthopedic spine surgeons have a higher number of publications, citations, and CJAL scores in spine research than those in neurosurgeons. Orthopedic spine surgeons tend to have more collaborations and coauthored papers in the field. The study highlights the differences in research productivity and collaboration patterns between the 2 authors in spine research and sheds light on potential contributing factors. The study recommends the use of FLCA for future bibliographical studies.

A comprehensive approach for clustering analysis using follower-leading clustering algorithm (FLCA): Bibliometric analysis.

BACKGROUND: There are 3 issues in bibliometrics that need to be addressed: The lack of a clear definition for author collaborations in cluster analysis that takes into account collaborations with and without self-connections; The need to develop a simple yet effective clustering algorithm for use in coword analysis, and; The inadequacy of general bibliometrics in regard to comparing research achievements and identifying articles that are worth reading and recommended for readers. The study aimed to put forth a clustering algorithm for cluster analysis (called following leader clustering [FLCA], a follower-leading clustering algorithm), examine the dissimilarities in cluster outcomes when considering collaborations with and without self-connections in cluster analysis, and demonstrate the application of the clustering algorithm in bibliometrics. METHODS: The study involved a search for articles and review articles published in JMIR Medical Informatics between 2016 and 2022, conducted using the Web of Science core collections. To identify author collaborations (ACs) and themes over the past 7 years, the study utilized the FLCA algorithm. With the 3 objectives of; Comparing the results obtained from scenarios with and without self-connections; Applying the FLCA algorithm in ACs and themes, and; Reporting the findings using traditional bibliometric approaches based on counts and citations, and all plots were created using R. RESULTS: The study found a significant difference in cluster outcomes between the 2 scenarios with and without self-connections, with a 53.8% overlap (14 out of the top 20 countries in ACs). The top clusters were led by Yonsei University in South Korea, Grang Luo from the US, and model in institutes, authors, and themes over the past 7 years. The top entities with the most publications in JMIR Medical Informatics were the United States, Yonsei University in South Korea, Medical School, and Grang Luo from the US. CONCLUSION: The FLCA algorithm proposed in this study offers researchers a comprehensive approach to exploring and comprehending the complex connections among authors or keywords. The study suggests that future research on ACs with cluster analysis should employ FLCA and R visualizations.

Global research trends in artificial intelligence for critical care with a focus on chord network charts: Bibliometric analysis.

BACKGROUND: The field of critical care-related artificial intelligence (AI) research is rapidly gaining interest. However, there is still a lack of comprehensive bibliometric studies that measure and analyze scientific publications on a global scale. Network charts have traditionally been used to highlight author collaborations and coword phenomena (ACCP). It is necessary to determine whether chord network charts (CNCs) can provide a better understanding of ACCP, thus requiring clarification. This study aimed to achieve 2 objectives: evaluate global research trends in AI in intensive care medicine on publication outputs, coauthorships between nations, citations, and co-occurrences of keywords; and demonstrate the use of CNCs for ACCP in bibliometric analysis. METHODS: The web of science database was searched for a total of 1992 documents published between 2013 and 2022. The document type was limited to articles and article reviews, and titles and abstracts were screened for eligibility. The characteristics of the publications, including preferred journals, leading research countries, international collaborations, top institutions, and major keywords, were analyzed using the category-journal rank-authorship-L-index score and trend analysis. The 100 most highly cited articles are also listed in detail. RESULTS: Between 2018 and 2022, there was a sharp increase in publications, which accounted for 92.8% (1849/1992) of all papers included in the study. The United States and China were responsible for nearly 50% (936/1992) of the total publications. The leading countries, institutes, departments, authors, and journals in terms of publications were the US, Massachusetts Gen Hosp (US), Medical School, Zhongheng Zhang (China), and Science Reports. The top 3 primary keywords denoting research hotspots for AI in critically ill patients were mortality, model, and intensive care unit, with mortality having the highest burst strength (4.49). The keywords risk and system showed the highest growth trend (0.98) in counts over the past 4 years. CONCLUSIONS: This study provides valuable insights into the potential for ACCP and future research opportunities. For AI-based clinical research to become widely accepted in critical care practice, collaborative research efforts are necessary to strengthen the maturity and robustness of AI-driven models using CNCs for display.

Visualizing burst spots on research for four authors in MDPI journals named to be Citation Laureates 2021 using temporal bar graph.

BACKGROUND: The appearance of a topic in a document stream is signaled by a burst of activity, with certain features rising sharply in frequency as the topic emerges. Although temporal bar graph (TBG) is frequently applied to present the burst spot in the bibliographical study, none of the research has combined the inflection point (IP) to interpret the burst spot feature. The aims of this study are to improve the traditional TBG and apply the TBG to understand better the evolution of a topic (e.g., publications and citations for a given author). METHODS: The EISTL model, including entity, indicator, selection of a few vital ones (named attributes) with higher values in quantity (e.g., the citation data of the top 10 entities), TBG and line-chart plots to verify the trend of interest, was proposed to demonstrate the TBG as a whole. The IP locations compared to the median point in data along with the heap map and line-chart trend were identified. The burst strength was computed. A dashboard on Google Maps was designed and launched for bibliometric analysis. Four authors in MDPI (Multidisciplinary Digital Publishing Institute) journals named to be Citation Laureates 2021 were recruited to compare their research achievements shown on the TBG, particularly displaying the burst spots and the recent developments and stages (e.g., increasing, ready to increase, slowdown, or decreasing). RESULTS: We observed that the highest burst strengths in publication and citations are earned by Barry Halliwell (8.99) and Jean-Pierre Changeux (18.01). The breakthrough of TBG using the EISTL model to display the influence of authors in academics was made with 2 parts of the primary IP point and the trend feature in the data. CONCLUSION: The dashboard-type TBG shown on Google Maps is unique and innovative and able to provide deeper insights to readers, not merely limited to the publications and citations for a given author as we did in this study.