bluuebunny/crossref_metadata_2025_split huggingface dataset:
Analysis of Scientific Publications During the Early Phase of the COVID-19 Pandemic: Topic Modeling Study
Authors: Älgå Andreas , Eriksson Oskar , Nordberg Martin 
Published: October 2020

Background
The COVID-19 pandemic has spread at an alarming speed, and effective treatment for the disease is still lacking. The body of evidence on COVID-19 has been increasing at an impressive pace, creating the need for a method to rapidly assess the current knowledge and identify key information. Gold standard methods such as systematic reviews and meta-analyses are regarded unsuitable because they have a narrow scope and are very time consuming.

Objective
This study aimed to explore the published scientific literature on COVID-19 and map the research evolution during the early phase of the COVID-19 pandemic.

Methods
We performed a PubMed search to analyze the titles, keywords, and abstracts of published papers on COVID-19. We used latent Dirichlet allocation modeling to extract topics and conducted a trend analysis to understand the temporal changes in research for each topic, journal impact factor (JIF), and geographic origin.

Results
Based on our search, we identified 16,670 relevant articles dated between February 14, 2020, and June 1, 2020. Of these, 6 articles were reports from peer-reviewed randomized trials on patients with COVID-19. We identified 14 main research topics, of which the most common topics were health care responses (2812/16,670, 16.86%) and clinical manifestations (1828/16,670, 10.91%). We found an increasing trend for research on clinical manifestations and protective measures and a decreasing trend for research on disease transmission, epidemiology, health care response, and radiology. Publications on protective measures, immunology, and clinical manifestations were associated with the highest JIF. The overall median JIF was 3.7 (IQR 2.6-5.9), and we found that the JIF for these publications declined over time. The top countries producing research were the United States, China, Italy, and the United Kingdom.

Conclusions
In less than 6 months since the novel coronavirus was first detected, a remarkably high number of research articles on COVID-19 have been published. Here, we discuss and present the temporal changes in the available COVID-19 research during the early phase of the pandemic. Our findings may aid researchers and policy makers to form a structured view of the current COVID-19 evidence base and provide further research directions.
Physical Distancing Measures and Walking Activity in Middle-aged and Older Residents in Changsha, China, During the COVID-19 Epidemic Period: Longitudinal Observational Study
Authors: Wang Yilun , Zhang Yuqing , Bennell Kim , White Daniel Kenta , Wei Jie , Wu Ziying , He Hongyi , Liu Shaohui , Luo Xianghang , Hu Shuo , Zeng Chao , Lei Guanghua 
Published: October 2020

Background
Physical distancing measures taken to contain COVID-19 transmission may substantially reduce physical activity levels and cause individuals to adopt a more sedentary lifestyle.

Objective
The objective of this study is to determine if there was any change in daily steps, an important component of daily physical activity, and examine risk factors for frequent low daily steps during the COVID-19 epidemic.

Methods
We used data collected from the Step Study, a population-based longitudinal study of walking activity among residents aged ≥40 years in Changsha, China. Daily steps were collected via a smartphone linked to WeChat, a social networking platform. We plotted mean daily steps and the prevalence of low daily steps (≤1500 steps/day) 30 days before (reference period) and 30 days after (epidemic period) January 21, 2020 (date of the first COVID-19 case diagnosed in Changsha), and compared it with the same corresponding period from 2019. We examined the association of risk factors with the prevalence of frequent low daily steps (≤1500 steps/day for ≥14 days) using logistic regression.

Results
Among 3544 participants (mean age 51.6 years; n=1226 females, 34.6%), mean daily steps dropped from 8097 to 5440 and the prevalence of low daily steps increased from 3% (2287/76,136 person-day) to 18.5% (12,951/70,183 person-day) during the reference and epidemic periods, respectively. No such phenomenon was observed during the corresponding period in 2019. Older age (P for interaction=.001) and female sex (P for interaction<.001) were both associated with a higher prevalence of frequent low daily steps and were more pronounced during the epidemic period. More education was associated with a lower prevalence of frequent low daily steps during the reference period but not the epidemic period (P for interaction=.34). Body mass index or comorbidity were not associated with frequent low daily steps during either period.

Conclusions
Daily steps of Changsha residents aged ≥40 years dropped significantly during the COVID-19 period, especially among older adults and females. Although successful physical distancing, measured by the rapid downward trend in daily step counts of residents, played a critical role in the containment of the COVID-19 epidemic, our findings of an increase in the prevalence of frequent low daily steps raise concerns about unintended effects on physical activity.
Prognostic Assessment of COVID-19 in the Intensive Care Unit by Machine Learning Methods: Model Development and Validation
Authors: Pan Pan , Li Yichao , Xiao Yongjiu , Han Bingchao , Su Longxiang , Su Mingliang , Li Yansheng , Zhang Siqi , Jiang Dapeng , Chen Xia , Zhou Fuquan , Ma Ling , Bao Pengtao , Xie Lixin 
Published: October 2020

Background
Patients with COVID-19 in the intensive care unit (ICU) have a high mortality rate, and methods to assess patients’ prognosis early and administer precise treatment are of great significance.

Objective
The aim of this study was to use machine learning to construct a model for the analysis of risk factors and prediction of mortality among ICU patients with COVID-19.

Methods
In this study, 123 patients with COVID-19 in the ICU of Vulcan Hill Hospital were retrospectively selected from the database, and the data were randomly divided into a training data set (n=98) and test data set (n=25) with a 4:1 ratio. Significance tests, correlation analysis, and factor analysis were used to screen 100 potential risk factors individually. Conventional logistic regression methods and four machine learning algorithms were used to construct the risk prediction model for the prognosis of patients with COVID-19 in the ICU. The performance of these machine learning models was measured by the area under the receiver operating characteristic curve (AUC). Interpretation and evaluation of the risk prediction model were performed using calibration curves, SHapley Additive exPlanations (SHAP), Local Interpretable Model-Agnostic Explanations (LIME), etc, to ensure its stability and reliability. The outcome was based on the ICU deaths recorded from the database.

Results
Layer-by-layer screening of 100 potential risk factors finally revealed 8 important risk factors that were included in the risk prediction model: lymphocyte percentage, prothrombin time, lactate dehydrogenase, total bilirubin, eosinophil percentage, creatinine, neutrophil percentage, and albumin level. Finally, an eXtreme Gradient Boosting (XGBoost) model established with the 8 important risk factors showed the best recognition ability in the training set of 5-fold cross validation (AUC=0.86) and the verification queue (AUC=0.92). The calibration curve showed that the risk predicted by the model was in good agreement with the actual risk. In addition, using the SHAP and LIME algorithms, feature interpretation and sample prediction interpretation algorithms of the XGBoost black box model were implemented. Additionally, the model was translated into a web-based risk calculator that is freely available for public usage.

Conclusions
The 8-factor XGBoost model predicts risk of death in ICU patients with COVID-19 well; it initially demonstrates stability and can be used effectively to predict COVID-19 prognosis in ICU patients.
openEHR Archetype Use and Reuse Within Multilingual Clinical Data Sets: Case Study
Authors: Leslie Heather 
Published: October 2020

Background
Despite electronic health records being in existence for over 50 years, our ability to exchange health data remains frustratingly limited. Commonly used clinical content standards, and the information models that underpin them, are primarily related to health data exchange, and so are usually document- or message-focused. In contrast, over the past 12 years, the Clinical Models program at openEHR International has gradually established a governed, coordinated, and coherent ecosystem of clinical information models, known as openEHR archetypes. Each archetype is designed as a maximal data set for a universal use-case, intended for reuse across various health data sets, known as openEHR templates. To date, only anecdotal evidence has been available to indicate if the hypothesis of archetype reuse across templates is feasible and scalable. As a response to the COVID-19 pandemic, between February and July 2020, 7 openEHR templates were independently created to represent COVID-19–related data sets for symptom screening, confirmed infection reporting, clinical decision support, and research. Each of the templates prioritized reuse of existing use-case agnostic archetypes found in openEHR International's online Clinical Knowledge Manager tool as much as possible. This study is the first opportunity to investigate archetype reuse within a range of diverse, multilingual openEHR templates.

Objective
This study aims to investigate the use and reuse of openEHR archetypes across the 7 openEHR templates as an initial investigation about the reuse of information models across data sets used for a variety of clinical purposes.

Methods
Analysis of both the number of occurrences of archetypes and patterns of occurrence within 7 discrete templates was carried out at the archetype or clinical concept level.

Results
Across all 7 templates collectively, 203 instances of 58 unique archetypes were used. The most frequently used archetype occurred 24 times across 4 of the 7 templates. Total data points per template ranged from 40 to 179. Archetype instances per template ranged from 10 to 62. Unique archetype occurrences ranged from 10 to 28. Existing archetype reuse of use-case agnostic archetypes ranged from 40% to 90%. Total reuse of use-case agnostic archetypes ranged from 40% to 100%.

Conclusions
Investigation of the amount of archetype reuse across the 7 openEHR templates in this initial study has demonstrated significant reuse of archetypes, even across unanticipated, novel modeling challenges and multilingual deployments. While the trigger for the development of each of these templates was the COVID-19 pandemic, the templates represented a variety of types of data sets: symptom screening, infection report, clinical decision support for diagnosis and treatment, and secondary use or research. The findings support the openEHR hypothesis that it is possible to create a shared, public library of standards-based, vendor-neutral clinical information models that can be reused across a diverse range of health data sets.
Geographical Appraisal of Gender Disparity and Progress in Literacy of Haryana, India
Authors: Sharma Manju , Kumar Sandeep 
Published: October 2020

The present research relates to the northern state of Haryana in India and to find out the objective of advancement and the existing gender gap in literacy with identification of responsible factors, the study uses the secondary data obtained from different censuses of India, National Crime Record Bureau and various other related sources. The disparity index is calculated to examine the gender gap in literacy whereas the correlation coefficient is used to ascertain its relationship with major determinants. Though the country and the state have achieved a reputed position with the literacy rate of 74.04 and 75.55 per cent respectively in 2011 yet inequality in the learning of male-female is a matter of concern for both entities. The figure for the district Mewat (which also lies at bottom in overall literacy as per ongoing census data) shows more discrimination in male-female learning, as here the male literacy rate is approximate to the double (69.97 per cent versus 36.60 per cent) of the females. To some extent, in areas like Mewat, Palwal and Fatehabad this discrimination is an outcome of societal stances, religion, fiscal or ethnical determinants and cultural stereotype as these factors of the environs have a direct or indirect association with literacy. So to ensure the equality and quality in education in basic or compulsory education, there is a need for massive investment on resources with communal awareness regarding significance as well as claptrap obstacles in the way of the learning.
Web-based decision aids to support breast cancer screening decisions: systematic review and meta-analysis
Authors: Yu Lin , Li Ping , Yang Shu , Guo Pingping , Zhang Xuehui , Liu Na , Wang Jie , Zhang Wei 
Published: October 2020

Aim:
Breast cancer is a leading cause of cancer among women. Because guidelines on screening for breast cancer for certain ages are controversial, many experts advocate the use of shared decision making (SDM) using validated decision aids (DAs). Recent studies have concluded that DAs are beneficial; however, the results have great heterogeneity. Therefore, further studies are needed to improve understanding of these tools.
Objective:
This systematic review and meta-analysis aimed to investigate the impact of using web-based DAs in women aged 50 years and below facing the decision to be screened for breast cancer in comparison with usual care.
Methods:
PubMed, Web of Science, Embase and the Cochrane CENTRAL databases were searched up to February 2020 for studies assessing web-based DAs for women making a breast cancer screening decision and reported quality of decision-making outcomes. Using a random-effects model or a fixed-effects model, meta-analyses were conducted pooling results using mean differences (MD), standardized mean differences (SMD) and relative risks (RR).
Results:
Of 1097 unique citations, three randomized controlled trials and two before–after studies met the study eligibility criteria. Compared with usual care, web-based DAs increased knowledge (SMD = 0.69; 95% CI: 0.57–0.80; p < 0.00001), reduced decision conflict and increased the proportion of women who made an informed choice (RR = 1.86; 95% CI: 1.38 to 2.50; p < 0.0001), but did not change the intention of women deciding to be screened or affect decision regret.
Conclusion:
This analysis showed the positive effect of web-based DAs on patient-centered outcomes in breast cancer screening. In the future, more internet devices and free or larger discount WI-FI should be established to ensure more women can benefit from this effective tool.
Reducing the Threat of Cyber Warfare Through a Suitable Dispute Resolution Mechanism
Authors: Khan Muhammad Asif 
Published: October 2020

With each step of technological advancement, we are entering a global technological domain susceptible to cyber infiltration. The individual privacy and security are supposed to be protected by the states governed by laws that are specifically a part of the national legal systems. The transnational cyber infiltration targeting the state actors by using the cyberspace creates a new plethora of questions. The issue has been highly debated, whether the jus ad bellum is sufficient in regulating the various types of cyber infiltrations. The matter of classifying the cyber-attacks as armed attacks has been furtively debated on contextual basis. The legal principles governing the laws of war have been held insufficient by some in order to include the new forms of attacks conducted through global cyberspace. In the midst of such debate, one conclusion can be derived that the cyber operations globally are causing a threat to state sovereignty and security. The focus on issues related to transnational cyber operations is based upon the existing legal principles and laws. The debate conjures up a few problems which need to be addressed. This article analyses the different perspectives of the cyber warfare and the identified problems related with the issue. According to the current problems faced by the states, a measure of the remedial system for states in international law is taken into consideration. The current system of remedies fails to accommodate the grievances of the states with regard to the cyber operations. Hence, a new platform for the state remedies is suggested and proposed.
Preventive Detention as a Personal Preventive Measure in Criminal Law of Chile
Authors: Torres López Hardy 
Published: October 2020

Two decades ago, Chile took action and started a process that modified its entire criminal system, a process that did not commence in Chile alone, but in the majority of South American countries who followed the same path; in Chile, the adversarial system was gradually implemented, and since 2005 it remains in force in every part of the country. Perhaps, the most important objective and/or effect that this change brought to the Chile’s criminal law was to ensure the complete respect towards the fundamental rights of those who were under investigation in the criminal system, overcoming the problems and obstacles that the previous inquisitorial system presented. Thereby, one of the aspects that this new criminal system improved was granting the authority to judges to issue a preventive detention as a personal preventive measure during the aftermath of the criminal process, whilst such measure was applied in a massive and general manner within the previous system. This article aims to present the main impacts that the application of this new system of criminal procedure has introduced in Chile, regarding the use of the preventive detention as a personal preventive measure during the development of the criminal processes. The author intends to examine whether the overhaul of the criminal procedure system in Chile has been a useful tool to streamline the use of the preventive detention measure in the criminal process, namely, if such change has been able to create a conduct consistent with the internationally recognized values and that should guide the use of this preventive measure, especially regarding its exceptional nature and the right to be considered innocent before trial. To comply with the previously stated purpose, this article is divided into two sections. The first one, beyond analysing the context of the preventive detention during the Chilean inquisitive system in the scope of the changes at a normative level that the new criminal procedure system proposes, is also used as a baseline for contrasting the outcomes obtained after implementation of these changes. The second section is dedicated to analysing the impact of the overhaul of the criminal procedure system with regard to applying the preventive detention in Chile and the series of changes that have been implemented.
Measuring a Business's Grit and Survivability when Faced with “Black Swan” Events Like the Coronavirus Pandemic
Authors: Gray Glen L. , Alles Michael G. 
Published: October 2020

ABSTRACT
The coronavirus crisis disrupted business survivability. Measures, like going concern opinion and bankruptcy predictors, depend on past trends extending into the future. With black swan events, past trends do not extend into the future. We propose two new metrics. The “Going Concern Survivability Index” (GCSI) is the maximum percentage revenue loss that a business can endure as a going concern. The “One Month Resilience Index” (OMRI) is the effect on the net income from the loss of the revenue for its most successful month. While OMRI is straightforward, calculating GCSI requires real options and process mining. The emerging technology of process mining and artificial intelligence are needed to capture the dynamic process by which management will juggle cash flows, sources of funds, and payment of liabilities as revenue falls. This paper is an instance of action design science research, and we discuss the steps to put our artifact into practice.
Improving the Accuracy of Fatigue Damage Calculations for Archived Data
Authors: Leski Andrzej 
Published: October 2020

Abstract
Historical operational usage data give a ground for fatigue damage estimation. Quality of sensors and recorders two or more decades ago were lower then modern one. Lack of resolution in nz-level measurement and recording leads to some errors in fatigue damage calculations. In this paper author propose a method to improve the accuracy of fatigue damage calculations for archived data. The method takes advantage of typical distribution of accumulated cycles for aircrafts. Small correction in representative nz value taken in calculations can reduce the error in fatigue damage assessment.
Low-Cost Data Acquisition Unit for Flight Tests
Authors: Morawski Błażej , Głowacki Dominik , Głowacka Anna 
Published: October 2020

Abstract
The purpose of this paper is to present the design of a data recorder for flight tests of a full-scale aircraft and an UAV. The recorder is built based on the Arduino microprocessor platform and LabVIEW development environment. The data recorder will be used mainly for helicopter flight tests.
Forensic anthropological video-based cases at the Department of Forensic Medicine, University of Copenhagen: a 10-year retrospective review
Authors: Nielsen Mark , Lynnerup Niels , Larsen Peter Kastmand 
Published: October 2020

Abstract
In criminal cases where there is a lack of evidence, the authorities sometimes ask our department to make a comparison of a criminal and suspect for possible identification or exclusion. This paper provides a comprehensive overview of 113 such cases analysed by the Department of Forensic Medicine, University of Copenhagen, Denmark in 2006–2016. The aims of the analyses were to assist the authorities in identifying individuals, predominantly due to a suspicion of involvement in criminal activity. The videos comprised surveillance footage showing a criminal committing a criminal act. Based on comparisons of specific parameters, such as face, gait and general body proportions, conclusions were drawn on the plausibly of a suspect being the perpetrator of the crime in the video footage. This paper describes the most typical case: a comparison of one suspect with one criminal committing a robbery. In the majority of cases, the suspect could not be excluded from being the criminal based on the analysis of the video footage.