bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Systematic Review and Meta-Analysis of the Safety of Hydroxychloroquine
in a Randomized Controlled Trial and Observational Studies
Authors: Bose Debdipta , Konwar Mahanjit , Maurya Miteshkumar , Thatte Urmila M. , Gogtay Nithya J. 
Published: July 2021

Introduction:
Hydroxychloroquine (HCQ) has recently become the focus of attention in the
current COVID-19 pandemic. With an increase in the off-label use of HCQ, concern for the safety of
HCQ has been raised. We, therefore, performed this systematic review to analyze the safety data of
HCQ against placebo and active treatment in various disease conditions.

Methods:
We searched PubMed, Embase, and Cochrane for Randomized Controlled Trials (RCTs)
and Observational Studies (OSs) that evaluated HCQ for the treatment of any disease other than
COVID19 in adult patients up to May 2020. We assessed the quality of the included studies using
Risk of Bias 2 (for RCTs) and Newcastle–Ottawa Scale (for OSs). Data were analyzed with randomeffect
meta-analysis. Sensitivity and subgroup analyses were performed to identify heterogeneity.

Results:
A total of 6641 studies were screened, and 49 studies (40 RCTs and 9 OSs) with a total sample
size of 35044 patients were included. The use of HCQ was associated with higher risks of TDAEs
as compared to placebo/no active treatment [RR 1.47, 95%CI 1.03-2.08]. When HCQ was compared
with active treatments, the risks of AEs [RR 0.74, 95% CI 0.63-0.86] and TDAEs were less in the
HCQ arm [RR 0.57, 95% CI 0.39-0.81]. The outcomes did not differ in the sensitivity analysis.

Conclusion:
The results suggest that the use of HCQ was associated with a lower risk of AEs and
TDAEs as compared to active treatment, whereas posing higher risk of TDAEs as compared to placebo.
Effect of Crude Leaves Extract of Bersama abyssinica on Blood Glucose
Level and Serum Lipid Level of Streptozotocin-Induced Diabetic Mice:
Evidence for in vivo Antidiabetic Activity
Authors: Kifle Zemene Demelash , Alehegn Agumas Alemu , Adugna Baye Yrga , Mekuria Abebe Basazn , Enyew Engidaw Fentahun 
Published: July 2021

Background:
Diabetes mellitus is one of the major and common metabolic and chronic
disorders in the world. Several medicinal plants have been used globally for the management of diabetes
mellitus. The current study aimed to study the antihyperglycemic and antihyperlipidemic effects
of Bersama abyssinica.

Methods:
Antidiabetic effect of 80% methanolic crude extract of Bersama abyssinica was studied
in a repeated dose-treated STZ-induced diabetic mice model. The activities of Bersama abyssinica
on serum lipid level and body weight were investigated on STZ-induced diabetic mice. Data were
analyzed using one-way ANOVA and were significant when the p-value was less than 0.05.

Results:
All doses of the crude 80% methanolic extract of Bersama abyssinica (100 mg/kg, 200
mg/kg, and 400 mg/kg) exhibited a noticeable BGL reduction when compared with baseline blood
glucose level and diabetic control on the 7th and 14th days of administration. Moreover, higher dose
of the extract (at 400 mg/kg) significantly (p < 0.001, 54.3%) decreased the BGL in STZ-induced
diabetic mice. The maximum decrement in fasting BGL was achieved at the 14th days: 34.92%,
41.10%, 54.30%, and 59.66%, respectively for BAC 100 mg/kg, BAC 200 mg/kg, BAC 400
mg/kg, and GLC 5 mg/kg treated groups. Bersama abyssinica also displayed a significant (p <
0.05) improvement of serum lipid levels and body weight.

Conclusion:
Bersama abyssinica crude extract exhibited a significant antidiabetic effect and prevented
body weight loss in streptozotocin-induced diabetic mice. The finding also confirmed the
valuable biochemical activity of Bersama abyssinica by improving serum lipid levels.
Prediction Model of Anastomotic Leakage Among Esophageal Cancer Patients After Receiving an Esophagectomy: Machine Learning Approach
Authors: Zhao Ziran , Cheng Xi , Sun Xiao , Ma Shanrui , Feng Hao , Zhao Liang 
Published: July 2021

Background
Anastomotic leakage (AL) is one of the severe postoperative adverse events (5%-30%), and it is related to increased medical costs in cancer patients who undergo esophagectomies. Machine learning (ML) methods show good performance at predicting risk for AL. However, AL risk prediction based on ML models among the Chinese population is unavailable.

Objective
This study uses ML techniques to develop and validate a risk prediction model to screen patients with emerging AL risk factors.

Methods
Analyses were performed using medical records from 710 patients who underwent esophagectomies at the National Clinical Research Center for Cancer between January 2010 and May 2015. We randomly split (9:1) the data set into a training data set of 639 patients and a testing data set of 71 patients using a computer algorithm. We assessed multiple classification tools to create a multivariate risk prediction model. Our ML algorithms contained decision tree, random forest, naive Bayes, and logistic regression with least absolute shrinkage and selection operator. The optimal AL prediction model was selected based on model evaluation metrics.

Results
The final risk panel included 36 independent risk features. Of those, 10 features were significantly identified by the logistic model, including aortic calcification (OR 2.77, 95% CI 1.32-5.81), celiac trunk calcification (OR 2.79, 95% CI 1.20-6.48), forced expiratory volume 1% (OR 0.51, 95% CI 0.30-0.89); TLco (OR 0.56, 95% CI 0.27-1.18), peripheral vascular disease (OR 4.97, 95% CI 1.44-17.07), laparoscope (OR 3.92, 95% CI 1.23-12.51), postoperative length of hospital stay (OR 1.17, 95% CI 1.13-1.21), vascular permeability activity (OR 0.46, 95% CI 0.14-1.48), and fat liquefaction of incisions (OR 4.36, 95% CI 1.86-10.21). Logistic regression with least absolute shrinkage and selection operator offered the highest prediction quality with an area under the receiver operator characteristic of 72% in the training data set. The testing model also achieved similar high performance.

Conclusions
Our model offered a prediction of AL with high accuracy, assisting in AL prevention and treatment. A personalized ML prediction model with a purely data-driven selection of features is feasible and effective in predicting AL in patients who underwent esophagectomy.
Understanding Adolescents’ Perceptions and Aspirations Towards Their Relationship With Personal Technology: Survey Study
Authors: Jee Muhammad , Khan Alsa , Nazneen Nazneen 
Published: July 2021

Background
Understanding adolescents' relationship with technology is a pressing topic in this digital era. There seem to be both beneficial and detrimental implications that originate from use of technology by adolescents. Approximately 95% of adolescents have access to a smartphone, and several studies show a positive correlation between screen addiction and trends of anxiety and depression. At the same time, research shows that two-thirds of adolescents believe that technology is a necessity for connecting and making new friends.

Objective
The aim of this formative study was to understand adolescents' perception of their own and others’ relationship with personal technology.

Methods
A survey was conducted with 619 adolescents ranging in age from 13 to 19 years. Adolescents were asked how they perceived the relationship with their personal technology, how they perceived others' (parents, siblings, or friends) relationship with personal technology, and how they wish to relate to their personal technology in the future.

Results
"Essential,” “Distractive,” and “Addictive” were the most commonly selected descriptors to describe both adolescents' own relationship with technology (essential: 106/619, 17.1%; distractive: 105/619, 17%; addictive: 88/619, 14.2% ) and others’ relationship as well (essential: 96/619, 15.6%; distractive: 88/619, 14.3%; addictive: 90/619, 14.5%). Adolescents selected “Provides an escape” more to describe their own relationship with technology. Whereas, they selected “It's just a tool” and “Creates Barrier” more to describe others' relationship with technology. These trends are consistent across ages and genders. In addition, adolescents' aspirations for their relationship with their personal technology varied across ages: 13 to 15-year olds' top choice was “best friend”, 16 to 17-year olds’ top choice was “I don't believe in personal connection with mobile technology,” and 18 to 19-year olds’ top choice was “My personal assistant.”

Conclusions
Our 3-lens method allows us to examine how adolescents perceive their relationship with personal technology in comparison to others, as well as their future technological aspirations. Our findings suggest that adolescents see both communalities as well as differences in their own and others' relationships with technology. Their future aspirations for personal technology vary across age and gender. These preliminary findings will be examined further in our follow-up research.
A New Remote Guided Method for Supervised Web-Based Cognitive Testing to Ensure High-Quality Data: Development and Usability Study
Authors: Leong Victoria , Raheel Kausar , Sim Jia Yi , Kacker Kriti , Karlaftis Vasilis M , Vassiliu Chrysoula , Kalaivanan Kastoori , Chen S H Annabel , Robbins Trevor W , Sahakian Barbara J , Kourtzi Zoe 
Published: July 2021

Background
The global COVID-19 pandemic has triggered a fundamental reexamination of how human psychological research can be conducted safely and robustly in a new era of digital working and physical distancing. Online web-based testing has risen to the forefront as a promising solution for the rapid mass collection of cognitive data without requiring human contact. However, a long-standing debate exists over the data quality and validity of web-based studies. This study examines the opportunities and challenges afforded by the societal shift toward web-based testing and highlights an urgent need to establish a standard data quality assurance framework for online studies.

Objective
This study aims to develop and validate a new supervised online testing methodology, remote guided testing (RGT).

Methods
A total of 85 healthy young adults were tested on 10 cognitive tasks assessing executive functioning (flexibility, memory, and inhibition) and learning. Tasks were administered either face-to-face in the laboratory (n=41) or online using remote guided testing (n=44) and delivered using identical web-based platforms (Cambridge Neuropsychological Test Automated Battery, Inquisit, and i-ABC). Data quality was assessed using detailed trial-level measures (missed trials, outlying and excluded responses, and response times) and overall task performance measures.

Results
The results indicated that, across all data quality and performance measures, RGT data was statistically-equivalent to in-person data collected in the lab (P>.40 for all comparisons). Moreover, RGT participants out-performed the lab group on measured verbal intelligence (P<.001), which could reflect test environment differences, including possible effects of mask-wearing on communication.

Conclusions
These data suggest that the RGT methodology could help ameliorate concerns regarding online data quality—particularly for studies involving high-risk or rare cohorts—and offer an alternative for collecting high-quality human cognitive data without requiring in-person physical attendance.
Stepwise-Hierarchical Pooled Analysis for Synergistic Interpretation of Meta-analyses Involving Randomized and Observational Studies: Methodology Development
Authors: Shin In-Soo , Rim Chai Hong 
Published: July 2021

Background
The necessity of including observational studies in meta-analyses has been discussed in the literature, but a synergistic analysis method for combining randomized and observational studies has not been reported. Observational studies differ in validity depending on the degree of the confounders’ influence. Combining interpretations may be challenging, especially if the statistical directions are similar but the magnitude of the pooled results are different between randomized and observational studies (the ”gray zone”).

Objective
To overcome these hindrances, in this study, we aim to introduce a logical method for clinical interpretation of randomized and observational studies.

Methods
We designed a stepwise-hierarchical pooled analysis method to analyze both distribution trends and individual pooled results by dividing the included studies into at least three stages (eg, all studies, balanced studies, and randomized studies).

Results
According to the model, the validity of a hypothesis is mostly based on the pooled results of randomized studies (the highest stage). Ascending patterns in which effect size and statistical significance increase gradually with stage strengthen the validity of the hypothesis; in this case, the effect size of the observational studies is lower than that of the true effect (eg, because of the uncontrolled effect of negative confounders). Descending patterns in which decreasing effect size and statistical significance gradually weaken the validity of the hypothesis suggest that the effect size and statistical significance of the observational studies is larger than the true effect (eg, because of researchers’ bias).

Conclusions
We recommend using the stepwise-hierarchical pooled analysis approach for meta-analyses involving randomized and observational studies.
The Role of Machine Learning in Diagnosing Bipolar Disorder: Scoping Review
Authors: Jan Zainab , AI-Ansari Noor , Mousa Osama , Abd-alrazaq Alaa , Ahmed Arfan , Alam Tanvir , Househ Mowafa 
Published: July 2021

Background
Bipolar disorder (BD) is the 10th most common cause of frailty in young individuals and has triggered morbidity and mortality worldwide. Patients with BD have a life expectancy 9 to 17 years lower than that of normal people. BD is a predominant mental disorder, but it can be misdiagnosed as depressive disorder, which leads to difficulties in treating affected patients. Approximately 60% of patients with BD are treated for depression. However, machine learning provides advanced skills and techniques for better diagnosis of BD.

Objective
This review aims to explore the machine learning algorithms used for the detection and diagnosis of bipolar disorder and its subtypes.

Methods
The study protocol adopted the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. We explored 3 databases, namely Google Scholar, ScienceDirect, and PubMed. To enhance the search, we performed backward screening of all the references of the included studies. Based on the predefined selection criteria, 2 levels of screening were performed: title and abstract review, and full review of the articles that met the inclusion criteria. Data extraction was performed independently by all investigators. To synthesize the extracted data, a narrative synthesis approach was followed.

Results
We retrieved 573 potential articles were from the 3 databases. After preprocessing and screening, only 33 articles that met our inclusion criteria were identified. The most commonly used data belonged to the clinical category (19, 58%). We identified different machine learning models used in the selected studies, including classification models (18, 55%), regression models (5, 16%), model-based clustering methods (2, 6%), natural language processing (1, 3%), clustering algorithms (1, 3%), and deep learning–based models (3, 9%). Magnetic resonance imaging data were most commonly used for classifying bipolar patients compared to other groups (11, 34%), whereas microarray expression data sets and genomic data were the least commonly used. The maximum ratio of accuracy was 98%, whereas the minimum accuracy range was 64%.

Conclusions
This scoping review provides an overview of recent studies based on machine learning models used to diagnose patients with BD regardless of their demographics or if they were compared to patients with psychiatric diagnoses. Further research can be conducted to provide clinical decision support in the health industry.
Comparison of the Differences Between Web-Based and Traditional Questionnaire Surveys in Pediatrics: Comparative Survey Study
Authors: Fang Heping , Xian Ruoling , Ma Zhuoying , Lu Mingyue , Hu Yan 
Published: July 2021

Background
A web-based survey is a novel method for data capture. Some studies have applied web-based surveys in pediatrics, but few of them have reported data on the differences between web-based and traditional questionnaire surveys.

Objective
The objective of our study was to evaluate the internal consistency of a web-based survey and compare it with a traditional questionnaire survey in pediatrics.

Methods
A convenience sample of caregivers was invited to participate in the survey on feeding patterns and their children’s eating behaviors if their children were aged 2 to 7 years. A web-based survey and a traditional questionnaire survey were carried out between October 2018 and July 2019. A total of 1085 caregivers were involved in this study, and they were divided into the following three groups based on methods and sources: (1) web-based survey from a web source, (2) web-based survey from a hospital source, and (3) traditional questionnaire survey from a hospital source. The data were then compared and analyzed.

Results
A total of 735 caregivers participated in the web-based survey and 350 caregivers participated in the traditional questionnaire survey, and 816 cases were then included in the analyses after data processing. The effective rate of the web-based survey was 70.1% (515/735), and the completeness rate of the traditional questionnaire survey was 86.0% (301/350). There were no significant differences between web-based surveys from different sources. However, demographic characteristics were significantly different between the web-based and traditional questionnaire surveys, mainly in terms of age and caregivers (χ²4=16.509, P=.002 and χ²4=111.464, P<.001, respectively). Caregivers of children aged 2 to 3 years and grandparents were more likely to respond to the web-based survey. Age-specific stratified analysis showed that the score of “monitoring” and the reporting rate of “poor appetite” in children aged 2 to 3 years were significantly higher in the web-based survey compared to the traditional questionnaire survey after adjusting for demographic characteristics.

Conclusions
A web-based survey could be a feasible tool in pediatric studies. However, differences in demographic characteristics and their possible impacts on the results should be considered in the analyses.
Response to Covid-19 Crisis: Recommendations and Health Policy Changes
Authors: Aseel Bin Sawad  , Fatema Turkistani  
Published: July 2021

The World Health Organization (WHO) declared the pandemic status of coronavirus disease of 2019 (COVID-19). The necessity for social distancing has prompted states to make policy changes. These policy changes are in line with the policies and recommendations set by the WHO, the Centers for Disease and Control Prevention (CDC), the United States Department of Health and Human Services (HHS), the Centers for Medicare and Medical Services (CMS), the Kaiser Permanente, the National Institutes of Health (NIH), the United States Food and Drug Administration (FDA), and the American Medical Association (AMA). The aim of this study is to discuss and analyze the effectiveness of many organizations in responding to the COVID-19 crisis. Three processes were involved in conducting the narrative review: (1) a search was conducted through the websites of eight organizations (CDC, HHS, CMS, Kaiser Permanente, NIH, WHO, FDA, and AMA), (2) Selection of relevant information related to the responses taken by the eight organizations between January 21, 2020 and April 24, 2020 were included, (3) the information selected from the websites of the eight organizations were recorded, summarized, and integrated in this paper.COVID-19 is a health problem that needs to be approached in a collaborative manner. All eight organizations that have taken action against the further spread of the virus. The different policy changes and/or recommendations from the eight organizations have one common ground and that is the necessity for social distancing. To prevent overcrowding in hospitals and in other healthcare facilities, the organizations have decided to expand telehealth coverage. In Conclusions Health-related and policy-issuing organizations provided policies that are aligned with one another in order to contain the spread of COVID-19.
Effect of Ultrasound Therapy and Cryotherapy over Taping Technique in Patients with Acute Lateral Ankle Sprain
Authors: Dr. Ramamurthy  , Dr. Jibi Paul  , Dr. Hepsibah Sharmil  , Dr. Sathya P  
Published: July 2021

The sudden twisting of ankle which overstretches the ligaments to torn or sprain around the ankle joint can lead to pain and swelling. Pain and swelling is one of the major causes of concern in patients with acute lateral ankle sprain while performing physical and daily activity. The study aim was to find the effect of ultrasound therapy and cryotherapy over taping technique among patients with acute lateral ankle sprain. A total of 30 subjects with acute lateral ankle sprain participated in this study. Group A was given ultrasound therapy with cryotherapy for 30 minutes; Group B was given taping technique additional to ultrasound and cryotherapy. Pre and post test was done after 10 sessions of treatment. The outcome was measured for the participants by VAS score. Student’s t-test, Mann Whitney test, Wilcoxon signed rank test were used to analyze data in this study. Group A and B have shown significant improvement after treatment. Comparative study found Group B, had significant changes by ultrasound therapy, cryotherapy and ankle taping technique over the Group A with P<0.001, on reducing pain, swelling and improving earlier active performance. On the basis of the result, the study concluded that treatment with cryotherapy, ultrasound therapy and taping technique is better compared with cryotherapy and ultrasound therapy, and has more effect in reducing pain, swelling and improving earlier active physical performance.
Reliability of Cobb’s Angle in a Measurement of Kyphosis – A Meta–Analysis
Authors: G.Vaishnavi  , S.Preethi  , G, Tharani  , V.Rajalaxmi  , K.Kirupa  , S.M.Divya  Mary  , RNV.Deepthi  , A.Sangeetha  , Rajashri.R  
Published: July 2021

This study aimed at analyzing the literature systematically on the reliability of Cobb’s angle in measurement of kyphosis. Cobb’s angle is the most widely used measurement to quantify spinal deformity. Kyphosis is the spinal disorder in which an excessive outwards of the spine results in an abnormal rounding of the upper back. Cobb’s angle used as standard measurement to determine and trackthe progression of scoliosis andkyphosis. Hence the purpose of the meta-analysis is to analyze the reliability of Cobb’s angle in a measurement of kyphosis Systematic searches in PubMed, Science direct, Google scholar, Cochrane library, Research Gate for systematic review was done. Research article and review article were selected within the published 1999 – 2020. The study design is Meta-analysis study of Analytical type. Study quality was done by PEDro Scale The study duration is about 4 months. A Meta-analysis was conducted by using Medical software to obtain a summary estimate of the standardized mean difference and 95% confidence limit. 100 articles taken for review in which finally 35 articles were summarized. Almost all the study shows that there was a highly significant difference on measurement of kyphosis using COBB’S angle. Outcome of measurement has excellent reliability with the small range of standard error of measurement. Cobb’s measurement had excellent reliability when used to measure kyphosis. The findings confirm the significance and the ease of using this method.