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.

The 95% control lines on both confirmed cases and days of infection with COVID-19 were applied to compare the impact on public health between 2020 and 2021 using the hT-index.

BACKGROUND: COVID-19, the disease caused by the novel coronavirus, is now a worldwide pandemic. The number of infected people has continually increased, and currently, this pandemic continues to present challenges to public health. Scatter plots are frequently used to interpret the impact in relation to confirmed cases. However, the 95% confidence intervals are rarely given to the scatter plot. The objective of this study was to; Develop 95% control lines on daily confirmed cases and infected days for countries/regions in COVID-19 (DCCIDC) and; Examine their impacts on public health (IPH) using the hT-index. METHODS: All relevant COVID-19 data were downloaded from GitHub. The hT-index, taking all DCCIDCs into account, was applied to measure the IPHs for counties/regions. The 95% control lines were proposed to highlight the outliers of entities in COVID-19. The hT-based IPHs were compared among counties/regions between 2020 and 2021 using the choropleth map and the forest plot. The features of the hT-index were explained using the line chart and the box plot. RESULTS: The top 2 countries measured by hT-based IPHs were India and Brazil in 2020 and 2021. The outliers beyond the 95% confidence intervals were Hubei (China), with a lower hT-index favoring 2021 ( = 6.4 in 2021 vs 15.55 in 2020) and higher hT indices favoring 2021 in Thailand (28.34 vs 14,77) and Vietnam (27.05 vs 10.88). Only 3 continents of Africa, Asia, and Europe had statistically and significantly fewer DCCIDCs (denoted by the hT-index) in 2021. The hT-index generalizes the h-index and overcomes the disadvantage without taking all elements (e.g., DCCIDCs) into account in features. CONCLUSIONS: The scatter plot combined with the 95% control lines was applied to compare the IPHs hit by COVID-19 and suggested for use with the hT-index in future studies, not limited to the field of public health as we did in this research.

Changes of sarcopenia case finding by different Asian Working Group for Sarcopenia in community indwelling middle-aged and old people.

Sarcopenia is an emerging issue, but there is no universal consensus regarding its screening and diagnosis, especially regarding the influence of the Asian Working Group for Sarcopenia (AWGS) 2019 new definition on the prevalence of community-dwelling adults. To compare the prevalence of sarcopenia between the 2019 and 2014 definitions, a cross-sectional study including 606 normal nutritional status subjects (203 men/403 women; mean age 63.3 ± 10.0 years) was performed. Sarcopenic parameters, including calf circumference, grip strength, 6-m gait speed, and bioelectrical-impedance-analysis-derived skeletal mass index (SMI), were evaluated. According to the 2019 AWGS definition, the prevalence of possible sarcopenia and sarcopenia among community-dwelling adults was 7.4 and 2.8%, respectively. There were highly consistent findings regarding sarcopenia between the 2019 and 2014 AWGS definitions according to Cohen's kappa coefficient (0.668). However, the prevalence of possible sarcopenia according to 2014 and 2019 AWGS in males increased 7.9%; in contrast, sarcopenia decreased from 7.4 to 3.7% in females (p < 0.001). In conclusion, the AWGS 2019 definition is more convenient for sarcopenia case screening and remains considerably consistent in sarcopenia identification in community-dwelling adults in Taiwan. The discordance of possible sarcopenia and sarcopenia by sex is a concern.

Comparison of prediction accuracies between two mathematical models for the assessment of COVID-19 damage at the early stage and throughout 2020.

BACKGROUND: The negative impacts of COVID-19 (ImpactCOVID) on public health are commonly assessed using the cumulative numbers of confirmed cases (CNCCs). However, whether different mathematical models yield disparate results based on varying time frames remains unclear. This study aimed to compare the differences in prediction accuracy between 2 proposed COVID-19 models, develop an angle index that can be objectively used to evaluate ImpactCOVID, compare the differences in angle indexes across countries/regions worldwide, and examine the difference in determining the inflection point (IP) on the CNCCs between the 2 models. METHODS: Data were downloaded from the GitHub website. Two mathematical models were examined in 2 time-frame scenarios during the COVID-19 pandemic (the early 20-day stage and the entire year of 2020). Angle index was determined by the ratio (=CNCCs at IP÷IP days). The R2 model and mean absolute percentage error (MAPE) were used to evaluate the model's prediction accuracy in the 2 time-frame scenarios. Comparisons were made using 3 visualizations: line-chart plots, choropleth maps, and forest plots. RESULTS: Exponential growth (EXPO) and item response theory (IRT) models had identical prediction power at the earlier outbreak stage. The IRT model had a higher model R2 and smaller MAPE than the EXPO model in 2020. Hubei Province in China had the highest angle index at the early stage, and India, California (US), and the United Kingdom had the highest angle indexes in 2020. The IRT model was superior to the EXPO model in determining the IP on an Ogive curve. CONCLUSION: Both proposed models can be used to measure ImpactCOVID. However, the IRT model (superior to EXPO in the long-term and Ogive-type data) is recommended for epidemiologists and policymakers to measure ImpactCOVID in the future.

The use of the time-to-event index (Tevent) to compare the negative impact of COVID-19 on public health among continents/regions in 2020 and 2021: An observational study.

BACKGROUND: During the COVID-19 pandemic, how to measure the negative impact caused by COVID-19 on public health (ImpactCOV) is an important issue. However, few studies have applied the bibliometric index, taking both infected days (quantity) and impact (damage) into account for evaluating ImpactCOV thus far. This study aims to verify the proposed the time-to-event index (Tevent) that is viable and applicable in comparison with 11 other indicators, apply the Tevent to compare the ImpactCOVs among groups in continents/countries in 2020 and 2021, and develop an online algorithm to compute the Tevent-index and draw the survival analysis. METHODS: We downloaded COVID-19 outbreak data of daily confirmed cases (DCCs) for all countries/regions. The Tevent-index was computed for each country and region. The impactCOVs among continents/countries were compared using the Tevemt indices for groups in 2020 and 2021. Three visualizations (i.e., choropleth maps, forest plot, and time-to-event, a.k.a. survival analysis) were performed. Online algorithms of Tevent as a composite score to denote the ImpactCOV and comparisons of Tevents for groups on Google Maps were programmed. RESULTS: We observed that the top 3 countries affected by COVID-19 in 2020 and 2021 were (India, Brazil, Russia) and (Brazil, India, and the UK), respectively; statistically significant differences in ImpactCOV were found among continents; and an online time-event analysis showed Hubei Province (China) with a Tevent of 100.88 and 6.93, respectively, in 2020 and 2021. CONCLUSION: The Tevent-index is viable and applicable to evaluate ImpactCOV. The time-to-event analysis as a branch of statistics for analyzing the expected duration of time until 1 event occurs is recommended to compare the difference in Tevent between groups in future research, not merely limited to ImpactCOV.

Effects of enriched branched-chain amino acid supplementation on sarcopenia.

To evaluate the effects of short-term administration of enriched branched-chain amino acids (BCAAs) on subjects with pre-sarcopenia or sarcopenia, our quasi-experimental study enrolled 33 subjects (12 pre-sarcopenia/21 sarcopenia; 6 men/27 women; mean age 66.6 ± 10.3 years) to take one sachet (3.6 g) of enriched BCAA powder twice a day for five weeks followed by a discontinuation period of 12 weeks. We evaluated sarcopenic parameters, including grip strength, 6-meter gait speed, and bioelectrical-impedance-analysis-derived skeletal mass index (SMI), at baseline, 5 weeks, and 17 weeks. We found that both pre-sarcopenic and sarcopenic subjects showed improved SMI, gait speed, and grip strength at 5 weeks. However, all three parameters progressively declined at 17 weeks, especially SMI and grip strength in subjects aged < 65 years and gait speed and grip strength in subjects aged ≥ 65 years. It thus appears that supplementation with enriched BCAAs for 5 weeks correlates with short-term positive effects on sarcopenic parameters but attenuation of those effects following discontinuation for 12 weeks.

Overexpression of the PSAT1 Gene in Nasopharyngeal Carcinoma Is an Indicator of Poor Prognosis.

PURPOSE: Nasopharyngeal carcinoma (NPC) is a common cancer in southern China and Southeast Asia, but risk stratification and treatment outcome in NPC patients remain suboptimal. Our study identified and validated metabolic drivers that are relevant to the pathogenesis of NPC using a published transcriptome. Phosphoserine aminotransferase 1 (PSAT1) is an enzyme that is involved in serine biosynthesis, and its overexpression is associated with colon cancer, non-small cell lung cancer and breast cancer. However, its expression has not been systemically evaluated in patients with NPC. MATERIALS AND METHODS: We evaluated two public transcriptomes of NPC tissues and benign nasopharyngeal mucosal epithelial tissues that deposited in the NIH Gene Expression Omnibus database under accession number GSE34574 and GSE12452. We also performed immunohistochemical staining and assessment of PSAT1 in a total of 124 NPC patients received radiotherapy and were regularly followed-up until death or loss. The endpoints analyzed were local recurrence-free survival (LRFS), distant metastasis-free survival (DMFS), disease-specific survival (DSS), and overall survival (OS). RESULTS: We retrospectively evaluated 124 patients with NPC and found that high PSAT1 expression was associated with poor prognosis of NPC and indicator of advanced tumor stage. High PSAT1 expression also correlated with an aggressive clinical course, with significantly shorter DSS (HR= 2.856, 95% CI 1.599 to 5.101), DMFS (HR= 3.305, 95% CI 1.720 to 6.347), LRFS (HR= 2.834, 95% CI 1.376 to 5.835), and OS HR= 2.935, 95% CI 1.646-5.234) in multivariate analyses. CONCLUSIONS: Our study showed that PSAT1 is a potential prognostic biomarker and higher expression of PSAT1 is associated with a poor prognosis in NPC.