Analyzing collaboration and impact: A bibliometric review of four highly published authors' research profiles on collaborative maps.

The concept of impact beam plots (IBPs) has been introduced in academia as a means to profile individual researchers. Despite its potential, there has been a lack of comprehensive analysis that evaluates the research profiles of highly published authors through the lens of collaborative maps. This study introduces a novel approach, the rating scale for research profiles (RSRP), to create collaborative maps for prolific authors. The initial hypothesis posited that each of the research profiles would attain a grade A, necessitating empirical verification. This research employed collaborative maps to analyze the publication patterns of authors using the Web of Science database, focusing on co-authorship patterns and the impact of their scholarly work. The study relied on various bibliometric indicators, such as publication count, citation metrics, h-index, and co-authorship networks, to provide a detailed assessment of the contributions made by each author in their field. Additionally, authors' IBPs were generated and assessed alongside collaborative maps, using a grading scale ranging from A (excellent) to F (lacking any articles as first or corresponding author). The analysis confirmed that all 4 research profiles achieved a grade A, with their centroids located in the third quadrant, indicating a high level of scholarly impact. The h-indexes for the authors were found to be 38, 51, 53, and 59, respectively. Notably, Dr Tseng from Taiwan showed a distinct pattern, with a significant number of solo-authored publications in the second quadrant, in contrast to the other 3 authors who demonstrated a greater emphasis on collaboration, as evidenced by their positioning in the first quadrant. The study successfully demonstrates that RSRP and IBPs can be effectively used to analyze and profile the research output of highly published authors through collaborative maps. The research confirms the initial hypothesis that all 4 profiles would achieve a grade A, indicating an excellent level of scholarly impact and a strong presence in their respective fields. The utility of collaborative maps can be applied to bibliometric indicators in assessing the contributions and impact of scholars in the academic community.

Instruction on the Scientific Method Provides (Some) Protection Against Illusions of Causality.

People tend to overestimate the efficacy of an ineffective treatment when they experience the treatment and its supposed outcome co-occurring frequently. This is referred to as the outcome density effect. Here, we attempted to improve the accuracy of participants' assessments of an ineffective treatment by instructing them about the scientific practice of comparing treatment effects against a relevant base-rate, i.e., when no treatment is delivered. The effect of these instructions was assessed in both a trial-by-trial contingency learning task, where cue administration was either decided by the participant (Experiments 1 & 2) or pre-determined by the experimenter (Experiment 3), as well as in summary format where all information was presented on a single screen (Experiment 4). Overall, we found two means by which base-rate instructions influence efficacy ratings for the ineffective treatment: 1) When information was presented sequentially, the benefit of base-rate instructions on illusory belief was mediated by reduced sampling of cue-present trials, and 2) When information was presented in summary format, we found a direct effect of base-rate instruction on reducing causal illusion. Together, these findings suggest that simple instructions on the scientific method were able to decrease participants' (over-)weighting of cue-outcome coincidences when making causal judgements, as well as decrease their tendency to over-sample cue-present events. However, the effect of base-rate instructions on correcting illusory beliefs was incomplete, and participants still showed illusory causal judgements when the probability of the outcome occurring was high. Thus, simple textual information about assessing causal relationships is partially effective in influencing people's judgements of treatment efficacy, suggesting an important role of scientific instruction in debiasing cognitive errors.

Uncertainty-modulated attentional capture: Outcome variance increases attentional priority.

Our prior experiences shape the way that we prioritize information from the environment for further processing, analysis, and action. We show in three experiments that this process of attentional prioritization is critically modulated by the degree of uncertainty in these previous experiences. Participants completed a visual search task in which they made a saccade to a target to earn a monetary reward. The color of a color-singleton distractor in the search array signaled the reward outcome(s) that were available, with different degrees of variance (uncertainty). Participants were never required to look at the colored distractor, and doing so would slow their response to the target. Nevertheless, across all experiments, participants were more likely to look at distractors associated with high outcome variance versus low outcome variance. This pattern was observed when all distractors had equal expected value (Experiment 1), when the difference in variance was opposed by a difference in expected value (i.e., the high-variance distractor had a low expected value, and vice versa: Experiment 2), and when high- and low-variance distractors were paired with the maximum-value outcome on an equal proportion of trials (Experiment 3). Our findings demonstrate that experience of prediction error plays a fundamental role in guiding "attentional exploration," wherein priority is driven by the potential for a stimulus to reduce future uncertainty through a process of learning, as opposed to maximizing current information gain. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Development of mobile CAT for patient feedback on pediatric consultations based on Rasch analysis of online techniques.

The Rasch Rating Scale Model (RSM) is widely used in questionnaire analysis, providing insights into how individuals respond to item-level stimuli. Existing software for Rasch RSM parameter estimation, while powerful, often presents a steep learning curve. An accessible online tool can greatly benefit novice users, particularly students and clinicians, by simplifying the analytical process. This study introduces an online tool, an intuitive online RSM analysis tool designed to facilitate questionnaire data analysis for applied researchers, students, and clinicians. The online tool employs the joint maximum likelihood method for estimation, yielding estimates, standard errors (SE), and fit statistics iteratively. A unique feature of the tool is its ability to visualize estimates on Google Maps with an opacity setting of 0, enhancing data interpretation through a user-friendly interface. This study outlines the estimation process and key features, employing data from 200 proxy participants who answered 20 5-point questions regarding doctor-patient and doctor-family interactions in pediatric consultations. Mobile computerized adaptive testing (CAT) was employed. The online tool offers 5 essential visual displays often utilized in Rasch analyses, including the Wright Map, KIDMAP, category probability curve, performance plot, and differential item functioning (DIF) graph. DIF analysis revealed that 2 items, concerning the doctor attentiveness and empathy toward the child illness, exhibited differences in female proxy participants' responses, indicating lower satisfaction with pediatricians. The online tool emerges as a user-friendly and efficient RSM analysis tool with notable advantages for newcomers, improving data visualization and comprehension. Its capacity to pinpoint key areas of concern, such as gender-related satisfaction disparities among proxy participants, enhances its utility in questionnaire analysis. The online tool holds promise as a valuable resource for researchers, students, and clinicians seeking accessible Rasch analysis solutions.

Developing a novel algorithm for comparing cluster patterns in networks on journal articles during and after COVID-19: Bibliometric analysis.

BACKGROUND: Cluster analysis is vital in bibliometrics for deciphering large sets of academic data. However, no prior research has employed a cluster-pattern algorithm to assess the similarities and differences between 2 clusters in networks. The study goals are 2-fold: to create a cluster-pattern comparison algorithm tailored for bibliometric analysis and to apply this algorithm in presenting clusters of countries, institutes, departments, authors (CIDA), and keywords on journal articles during and after COVID-19. METHODS: We analyzed 9499 and 5943 articles from the Journal of Medicine (Baltimore) during and after COVID-19 in 2020 to 2021 and 2022 to 2023, sourced from the Web of Science (WoS) Core Collection. Follower-leading clustering algorithm (FLCA) was compared to other 8 counterparts in cluster validation and effectiveness and a cluster-pattern-comparison algorithm (CPCA) was developed using the similarity coefficient, collaborative maps, and thematic maps to evaluate CIDA cluster patterns. The similarity coefficients were categorized as identical, similar, dissimilar, or different for values above 0.7, between 0.5 and 0.7, between 0.3 and 0.5, and below 0.3, respectively. RESULTS: Both stages displayed similar trends in annual publications and average citations, although these trends are decreasing. The peak publication year was 2020. Similarity coefficients of cluster patterns in these 2 stages for CIDA entities and keywords were 0.73, 0.35, 0.80, 0.02, and 0.83, respectively, suggesting the existence of identical patterns (>0.70) in countries, departments, and keywords plus, but dissimilar (<0.5) and different patterns (<0.3) found in institutes and 1st and corresponding authors, during and after COVID-19. CONCLUSIONS: This research effectively created and utilized CPCA to analyze cluster patterns in bibliometrics. It underscores notable identical patterns in country-/department-/keyword based clusters, but dissimilar and different in institute-/author- based clusters, between these 2 stages during and after COVID-19, offering a framework for future bibliographic studies to compare cluster patterns beyond just the CIDA entities, as demonstrated in this study.

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.

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.

Using unobserved causes to explain unexpected outcomes: The effect of existing causal knowledge on protection from extinction by a hidden cause.

People often rely on the covariation between events to infer causality. However, covariation between cues and outcomes may change over time. In the associative learning literature, extinction provides a model to study updating of causal beliefs when a previously established relationship no longer holds. Prediction error theories can explain both extinction and protection from extinction when an inhibitory (preventive) cue is present during extinction. In three experiments using the allergist causal learning task, we found that protection could also be achieved by a hidden cause that was inferred but not physically present, so long as that cause was a plausible preventer of the outcome. We additionally showed complete protection by a physically presented cue that was neutral rather than inhibitory at the outset of extinction. Both findings are difficult to reconcile with dominant prediction error theories. However, they are compatible with the idea of theory protection, where the learner attributes the absence of the outcome to the added cue (when present) or to a hidden cause, and therefore does not need to revise causal beliefs about A. Our results suggest that prediction error encourages changes in causal beliefs, but the nature of the change is determined by reasoning processes that incorporate existing knowledge of causal mechanisms and may be biased toward preservation of existing beliefs. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

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.

Emergence and dissemination of multidrug-resistant Escherichia coli ST8346 coharboring blaNDM-5 and blaOXA-181 in Southern Taiwan, 2017-2021.

BACKGROUND: Enterobacterales carrying blaNDM represent an emerging challenge in treating infectious diseases. In this study, we aimed to investigate the characteristics of blaNDM-producing Enterobacterales from three hospitals in southern Taiwan. METHODS: Enterobacterales strains that were nonsusceptible to more than one carbapenem (ertapenem, meropenem, imipenem, or doripenem) were collected from hospitalized patients. Molecular typing for New Delhi metallo-ß-lactamase (NDM) and antibiotic susceptibility tests were performed, followed by multilocus sequence typing (MLST), pulsed-field gel electrophoresis (PFGE), and plasmid analysis by PCR-based replicon typing. RESULTS: A total of 1311 carbapenem-nonsusceptible Enterobacterales were isolated from 2017 to 2021. blaNDM-encoding genes were detected in 108 isolates, with 53 (49.1%) harboring blaNDM-1 and 55 (50.9%) harboring blaNDM-5. The rate of blaNDM-1 detection among isolates decreased to 2% in 2021. However, the rate of E. coli harboring blaNDM-5 increased from 1% to 12% of total isolates during the study period. Of 47 NDM-5-positive E. coli isolates, 44 (93.6%) were ST8346 with high genetic relatedness. E. coli ST8346 isolates showed high-level resistance to both carbapenems and aminoglycosides. Most (38 out of 47, 80.9%) blaNDM-5-harboring E. coli isolates co-harbored blaOXA-181. We analyzed the regions harboring blaNDM-5 in E. coli ST8346 via PCR amplification. blaNDM-5 and blaOXA-181 were located on two separate plasmids, IncF and IncX3, respectively. CONCLUSION: The dissemination of E. coli ST8346 caused an increase in blaNDM-5 and blaOXA-181 co-harboring Enterobacterales in southern Taiwan, which show high-level resistance to both carbapenems and aminoglycosides. We identified a distinct IncF plasmid encoding blaNDM-5 that has the potential for rapid spread and needs further surveillance.

Real-time artificial intelligence system for bacteremia prediction in adult febrile emergency department patients.

BACKGROUND: Artificial intelligence (AI) holds significant potential to be a valuable tool in healthcare. However, its application for predicting bacteremia among adult febrile patients in the emergency department (ED) remains unclear. Therefore, we conducted a study to provide clarity on this issue. METHODS: Adult febrile ED patients with blood cultures at Chi Mei Medical Center were divided into derivation (January 2017 to June 2019) and validation groups (July 2019 to December 2020). The derivation group was utilized to develop AI models using twenty-one feature variables and five algorithms to predict bacteremia. The performance of these models was compared with qSOFA score. The AI model with the highest area under the receiver operating characteristics curve (AUC) was chosen to implement the AI prediction system and tested on the validation group. RESULTS: The study included 5,647 febrile patients. In the derivation group, there were 3,369 patients with a mean age of 61.4 years, and 50.7% were female, including 508 (13.8%) with bacteremia. The model with the best AUC was built using the random forest algorithm (0.761), followed by logistic regression (0.755). All five models demonstrated better AUC than the qSOFA score (0.560). The random forest model was adopted to build a real-time AI prediction system integrated into the hospital information system, and the AUC achieved 0.709 in the validation group. CONCLUSION: The AI model shows promise to predict bacteremia in adult febrile ED patients; however, further external validation in different hospitals and populations is necessary to verify its effectiveness.

Analyzing author collaborations by developing a follower-leader clustering algorithm and identifying top co-authoring countries: Cluster analysis.

BACKGROUND: This study aimed to explore suitable clustering algorithms for author collaborations (ACs) in bibliometrics and investigate which countries frequently coauthored with others in recent years. To achieve this, the study developed a method called the Follower-Leading Clustering Algorithm (FLCA) and used it to analyze ACs and cowords in the Journal of Medicine (Baltimore) from 2020 to 2022. METHODS: This study extracted article metadata from the Web of Science and used the statistical software R to implement FLCA, enabling efficient and reproducible analysis of ACs and cowords in bibliometrics. To determine the countries that easily coauthored with other countries, the study observed the top 20 countries each year and visualized the results using network charts, heatmaps with dendrograms, and Venn diagrams. The study also used chord diagrams to demonstrate the use of FLCA on ACs and cowords in Medicine (Baltimore). RESULTS: The study observed 12,793 articles, including 5081, 4418, and 3294 in 2020, 2021, and 2022, respectively. The results showed that the FLCA algorithm can accurately identify clusters in bibliometrics, and the USA, China, South Korea, Japan, and Spain were the top 5 countries that commonly coauthored with others during 2020 and 2022. Furthermore, the study identified China, Sichuan University, and diagnosis as the leading entities in countries, institutes, and keywords based on ACs and cowords, respectively. The study highlights the advantages of using cluster analysis and visual displays to analyze ACs in Medicine (Baltimore) and their potential application to coword analysis. CONCLUSION: The proposed FLCA algorithm provides researchers with a comprehensive means to explore and understand the intricate connections between authors or keywords. Therefore, the study recommends the use of FLCA and visualizations with R for future research on ACs with cluster analysis.

Switching from Sugar- to Artificially-Sweetened Beverages: A 12-Week Trial.

BACKGROUND: Consumption of sugar-sweetened beverages (SSBs) forms the primary source of added sugar intake and can increase the risk of metabolic disease. Evidence from studies in humans and rodents also indicates that consumption of SSBs can impair performance on cognitive tests, but that removing SSB access can ameliorate these effects. METHODS: The present study used an unblinded 3-group parallel design to assess the effects of a 12-week intervention in which young healthy adults (mean age = 22.85, SD = 3.89; mean BMI: 23.2, SD = 3.6) who regularly consumed SSBs were instructed to replace SSB intake with artificially-sweetened beverages (n = 28) or water (n = 25), or (c) to continue SSB intake (n = 27). RESULTS: No significant group differences were observed in short-term verbal memory on the Logical Memory test or the ratio of waist circumference to height (primary outcomes), nor in secondary measures of effect, impulsivity, adiposity, or glucose tolerance. One notable change was a significant reduction in liking for strong sucrose solutions in participants who switched to water. Switching from SSBs to 'diet' drinks or water had no detectable impact on cognitive or metabolic health over the relatively short time frame studied here. This study was prospectively registered with the Australian New Zealand Clinical Trials Registry (ACTRN12615001004550; Universal Trial Number: U1111-1170-4543).

The 10 top-cited authors who published papers in journal medicine since 2000 using the betweenness centrality to identify unique names: Bibliometric analysis.

BACKGROUND: Numerous studies have explored the most productive and influential authors in a specific field. However, 2 challenges arise when conducting such research. First, some authors may have identical names in the study data, and second, the contributions of coauthors may vary in the article by line, requiring consideration. Failure to address these issues may result in biased research findings. Our objective was to illustrate how the author-weighted scheme (AWS) and betweenness centrality (BC) can be employed to identify the 10 most frequently cited authors in a particular journal and analyze their research themes. METHODS: We collected 24,058 abstracts from the PubMed library between 2000 and 2020 using the keyword "Medicine [Journal]." Author names, countries/regions, and medical subject headings (MeSH terms) were collected. The AWS to identify the top 10 authors with a higher x-index was applied. To address the issue of authors with identical names affiliated with different research institutes, we utilized the BC method. Social network analysis (SNA) was conducted, and 10 major clusters were identified to highlight authors with a higher x-index within the corresponding clusters. We utilized SNA to analyze the MeSH terms from articles of the 10 top-cited authors to identify their research themes. RESULTS: Our findings revealed the following: within the top 10 cited authors, 2 authors from China shared identical names with Jing Li and Tao-Wang; JA Winkelstein from Maryland (US) had the highest x-index (15.58); Chia-Hung Kao from Taiwan was the most prolific author, having published 115 articles in Medicine since 2003; and the 3 primary research themes, namely, complications, etiology, and epidemiology, were identified using MeSH terms from the 10 most frequently cited authors. CONCLUSIONS: Using AWS and BC, we identified the top 10 most cited authors. The research methods we utilized in this study (BC and AWS) have the potential to be applied to other bibliometric analyses in the future.

Assessing ChatGPT's capacity for clinical decision support in pediatrics: A comparative study with pediatricians using KIDMAP of Rasch analysis.

BACKGROUND: The application of large language models in clinical decision support (CDS) is an area that warrants further investigation. ChatGPT, a prominent large language models developed by OpenAI, has shown promising performance across various domains. However, there is limited research evaluating its use specifically in pediatric clinical decision-making. This study aimed to assess ChatGPT's potential as a CDS tool in pediatrics by evCDSaluating its performance on 8 common clinical symptom prompts. Study objectives were to answer the 2 research questions: the ChatGPT's overall grade in a range from A (high) to E (low) compared to a normal sample and the difference in assessment of ChatGPT between 2 pediatricians. METHODS: We compared ChatGPT's responses to 8 items related to clinical symptoms commonly encountered by pediatricians. Two pediatricians independently assessed the answers provided by ChatGPT in an open-ended format. The scoring system ranged from 0 to 100, which was then transformed into 5 ordinal categories. We simulated 300 virtual students with a normal distribution to provide scores on items based on Rasch rating scale model and their difficulties in a range between -2 to 2.5 logits. Two visual presentations (Wright map and KIDMAP) were generated to answer the 2 research questions outlined in the objectives of the study. RESULTS: The 2 pediatricians' assessments indicated that ChatGPT's overall performance corresponded to a grade of C in a range from A to E, with average scores of -0.89 logits and 0.90 logits (=log odds), respectively. The assessments revealed a significant difference in performance between the 2 pediatricians (P < .05), with scores of -0.89 (SE = 0.37) and 0.90 (SE = 0.41) in log odds units (logits in Rasch analysis). CONCLUSION: This study demonstrates the feasibility of utilizing ChatGPT as a CDS tool for patients presenting with common pediatric symptoms. The findings suggest that ChatGPT has the potential to enhance clinical workflow and aid in responsible clinical decision-making. Further exploration and refinement of ChatGPT's capabilities in pediatric care can potentially contribute to improved healthcare outcomes and patient management.

The ascendancy of research in acronyms related to COVID-19 displayed on a growth-share matrix (GSM): Bibliometric analysis.

BACKGROUND: The acronym COVID, which stands for coronavirus disease, has become one of the most infamous acronyms in the world since 2020. An analysis of acronyms in health and medical journals has previously found that acronyms have become more common in titles and abstracts over time (e.g., DNA and human immunodeficiency virus are the most common acronyms). However, the trends in acronyms related to COVID remain unclear. It is necessary to verify whether the dramatic rise in COVID-related research can be observed by visualizations. The purpose of this study was to display the acronym trends in comparison through the use of temporal graphs and to verify that the COVID acronym has a significant edge over the other 2 in terms of research dominance. METHODS: An analysis of the 30 most frequently used acronyms related to COVID in PubMed since 1950 was carried out using 4 graphs to conduct this bibliometric analysis, including line charts, temporal bar graphs (TBGs), temporal heatmaps (THM), and growth-share matrices (GSM). The absolute advantage coefficient (AAC) was used to measure the dominance strength for COVID acronym since 2020. COVID's AAC trend was expected to decline over time. RESULTS: This study found that COVID, DNA, and human immunodeficiency virus have been the most frequently observed research acronyms since 2020, followed by computed tomography and World Health Organization; although there is no ideal method for displaying acronym trends over time, researchers can utilize the GSM to complement traditional line charts, TBGs, and THMs, as shown in this study; and COVID has a significant edge over the other 2 in terms of research dominance by ACC (≥0.67), but COVID's AAC trend has declined (e.g., AACs 0.83, 0.80, and 0.69) since 2020. CONCLUSIONS: It is recommended that the GSM complement traditional line charts, TBGs, and THMs in trend analysis, rather than being restricted to acronyms in future research. This research provides readers with the AAC to understand how research dominates its counterparts, which will be useful for future bibliometric analyses.

A leading author of meta-analysis does not have a dominant contribution to research based on the CJAL score: Bibliometric analysis.

BACKGROUND: There have been nearly 200 thousand meta-analysis articles indexed by web of science (WoS) since 2013. To date, a bibliometric analysis of leading authors of meta-analyses that contribute to the field has not been conducted. Analyzing trend patterns in article citations and comparing individual research achievements (IRAs) are required following the extraction of meta-analysis articles. Using trend analysis, this study aims to verify the hypotheses that; The leading author has a dominant research achievement and; Recent articles that deserve worth reading can be identified. METHODS: In the WoS collection, we identified the top 20 authors with the most articles related to meta-analysis. Using coword analysis, 2882 articles were collected to cluster author collaborations and identify the top 3 authors with the highest weighted centrality degrees. Based on the CJAL (category, journal raking by impact factor, authorship, and L-index on article citation) score and absolute advantage coefficient (AAC), we compared the IRAs and identified the author who dominated the field significantly beyond the next 2 authors. In WoS collection, coword analysis was used to highlight the characteristics of research domains for the top authors contributing to meta-analyses. The selection of articles that deserve reading is based on a temporal heatmap. RESULTS: The top 2 authors were Young-Ho Lee (South Korea), Patompong Ungprasert (U.S.), and Brendon Stubbs (US) with CJAL scores of 240.71, 230.99, and 240.71, respectively. Based on the weak dominance coefficient (AAC = 0.49 < 0.50), it is evident that the leading meta-analysis author does not possess a significant dominant position over the next 2 leading authors in IRAs. Coword analysis was used to illustrate the characteristics of the 3 authors research domains. The 3 articles worth reading were selected based on a trend analysis of the last 4 years using the temporal heatmap. CONCLUSION: A coword analysis of meta-analysis studies identified 3 leading authors. There was no evidence that 1 author possessed a dominant position due to the lower AAC (=0.49 < 0.50) for the leading author. As we have demonstrated in this study, the CJAL score and the AAC can be applied to many bibliographical studies in the future.

Retardation of acquisition after conditioned inhibition and latent inhibition training in human causal learning.

Inhibitory stimuli are slow to acquire excitatory properties when paired with the outcome in a retardation test. However, this pattern is also seen after simple nonreinforced exposure: latent inhibition. It is commonly assumed that retardation would be stronger for a conditioned inhibitor than for a latent inhibitor, but there is surprisingly little empirical evidence comparing the two in either animals or humans. Thus, retardation after inhibitory training could in principle be attributable entirely to latent inhibition. We directly compared the speed of excitatory acquisition after conditioned inhibition and matched latent inhibition training in human causal learning. Conditioned inhibition training produced stronger transfer in a summation test, but the two conditions did not differ substantially in a retardation test. We offer two explanations for this dissociation. One is that learned predictiveness attenuated the latent inhibition that otherwise would have occurred during conditioned inhibition training, so that retardation in that condition was primarily due to inhibition. The second explanation is that inhibitory learning in these experiments was hierarchical in nature, similar to negative occasion-setting. By this account, the conditioned inhibitor was able to negatively modulate the test excitor in a summation test, but was no more retarded than a latent inhibitor in its ability to form a direct association with the outcome. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Inhibitory Learning with Bidirectional Outcomes: Prevention Learning or Causal Learning in the Opposite Direction?

Influential models of causal learning assume that learning about generative and preventive relationships are symmetrical to each other. That is, a preventive cue directly prevents an outcome from occurring (i.e., "direct" prevention) in the same way a generative cue directly causes an outcome to occur. However, previous studies from our lab have shown that many participants do not infer a direct prevention causal structure after feature-negative discrimination (A+/AB-) with a unidirectional outcome (Lee & Lovibond, 2021). Melchers et al. (2006) suggested that the use of a bidirectional outcome that can either increase or decrease from baseline, encourages direct prevention learning. Here we test an alternative possibility that a bidirectional outcome encourages encoding of a generative relationship in the opposite direction, where B directly causes a decrease in the outcome. Thus, previous evidence of direct prevention learning using bidirectional outcomes may instead be explained by some participants inferring an "Opposite Causal" structure. In two experiments, participants did indeed report an opposite causal structure. In Experiment 1, these participants showed the lowest outcome predictions when B was combined with a novel cause in a summation test, and lowest outcome predictions when B was presented alone. In Experiment 2, B successfully blocked learning to a novel cue that was directly paired with a reduction in the outcome, and this effect was strongest among participants who endorsed an Opposite Causal structure. We conclude that previous evidence of direct prevention learning using bidirectional outcomes may be a product of excitatory rather than inhibitory learning.

Detecting dengue fever in children using online Rasch analysis to develop algorithms for parents: An APP development and usability study.

BACKGROUND: Dengue fever (DF) is a significant public health concern in Asia. However, detecting the disease using traditional dichotomous criteria (i.e., absent vs present) can be extremely difficult. Convolutional neural networks (CNNs) and artificial neural networks (ANNs), due to their use of a large number of parameters for modeling, have shown the potential to improve prediction accuracy (ACC). To date, there has been no research conducted to understand item features and responses using online Rasch analysis. To verify the hypothesis that a combination of CNN, ANN, K-nearest-neighbor algorithm (KNN), and logistic regression (LR) can improve the ACC of DF prediction for children, further research is required. METHODS: We extracted 19 feature variables related to DF symptoms from 177 pediatric patients, of whom 69 were diagnosed with DF. Using the RaschOnline technique for Rasch analysis, we examined 11 variables for their statistical significance in predicting the risk of DF. Based on 2 sets of data, 1 for training (80%) and the other for testing (20%), we calculated the prediction ACC by comparing the areas under the receiver operating characteristic curve (AUCs) between DF + and DF- in both sets. In the training set, we compared 2 scenarios: the combined scheme and individual algorithms. RESULTS: Our findings indicate that visual displays of DF data are easily interpreted using Rasch analysis; the k-nearest neighbors algorithm has a lower AUC (<0.50); LR has a relatively higher AUC (0.70); all 3 algorithms have an almost equal AUC (=0.68), which is smaller than the individual algorithms of Naive Bayes, LR in raw data, and Naive Bayes in normalized data; and we developed an app to assist parents in detecting DF in children during the dengue season. CONCLUSION: The development of an LR-based APP for the detection of DF in children has been completed. To help patients, family members, and clinicians differentiate DF from other febrile illnesses at an early stage, an 11-item model is proposed for developing the APP.