bluuebunny/crossref_metadata_2025_split huggingface dataset:
The Uncounted Casualties of a Hidden COVID-19 Epidemic in China: Cross-sectional Study on Deaths Related to Overwork
Authors: Wang Zhicheng , Lin Leesa , Guo Yan , Xiong Huayi , Tang Kun 
Published: April 2021

Background
During the COVID-19 response, nonclinical essential workers usually worked overtime and experienced significant work stress, which subsequently increased their risk of mortality due to cardiovascular diseases, stroke, and pre-existing conditions. Deaths on duty, including deaths due to overwork, during the COVID-19 response were usually reported on web-based platforms for public recognition and solidarity. Although no official statistics are available for these casualties, a list of on-duty deaths has been made publicly available on the web by crowdsourcing.

Objective
This study aims to understand the trends and characteristics of deaths related to overwork among the frontline nonclinical essential workers participating in nonpharmaceutical interventions during the first wave of COVID-19 in China.

Methods
Based on a web-based crowdsourced list of deaths on duty during the first wave of the COVID-19 response in China, we manually verified all overwork-related death records against the full-text web reports from credible sources. After excluding deaths caused by COVID-19 infection and accidents, a total of 340 deaths related to overwork among nonclinical essential workers were attributed to combatting the COVID-19 crisis. We coded the key characteristics of the deceased workers, including sex, age at death, location, causes of death, date of incidence, date of death, containment duties, working area, and occupation. The temporal and spatial correlations between deaths from overwork and COVID-19 cases in China were also examined using Pearson correlation coefficient.

Results
From January 20 to April 26, 2020, at least 340 nonclinical frontline workers in China were reported to have died as a result of overwork while combatting COVID-19. The weekly overwork mortality was positively correlated with weekly COVID-19 cases (r=0.79, P<.001). Two-thirds of deceased workers (230/340, 67.6%) were under 55 years old, and two major causes of deaths related to overwork were cardiovascular diseases (138/340, 40.6%) and cerebrovascular diseases (73/340, 21.5%). Outside of Hubei province, there were almost 2.5 times as many deaths caused by COVID-19–related overwork (308/340, 90.6%) than by COVID-19 itself (n=120).

Conclusions
The high number of deaths related to overwork among nonclinical essential workers at the frontline of the COVID-19 epidemic is alarming. Policies for occupational health protection against work hazards should therefore be prioritized and enforced.
Information Quality Frameworks for Digital Health Technologies: Systematic Review
Authors: Fadahunsi Kayode Philip , O'Connor Siobhan , Akinlua James Tosin , Wark Petra A , Gallagher Joseph , Carroll Christopher , Car Josip , Majeed Azeem , O'Donoghue John 
Published: April 2021

Background
Digital health technologies (DHTs) generate a large volume of information used in health care for administrative, educational, research, and clinical purposes. The clinical use of digital information for diagnostic, therapeutic, and prognostic purposes has multiple patient safety problems, some of which result from poor information quality (IQ).

Objective
This systematic review aims to synthesize an IQ framework that could be used to evaluate the extent to which digital health information is fit for clinical purposes.

Methods
The review was conducted according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) guidelines. We searched Embase, MEDLINE, PubMed, CINAHL, Maternity and Infant Care, PsycINFO, Global Health, ProQuest Dissertations and Theses Global, Scopus, and HMIC (the Health Management Information Consortium) from inception until October 2019. Multidimensional IQ frameworks for assessing DHTs used in the clinical context by health care professionals were included. A thematic synthesis approach was used to synthesize the Clinical Information Quality (CLIQ) framework for digital health.

Results
We identified 10 existing IQ frameworks from which we developed the CLIQ framework for digital health with 13 unique dimensions: accessibility, completeness, portability, security, timeliness, accuracy, interpretability, plausibility, provenance, relevance, conformance, consistency, and maintainability, which were categorized into 3 meaningful categories: availability, informativeness, and usability.

Conclusions
This systematic review highlights the importance of the IQ of DHTs and its relevance to patient safety. The CLIQ framework for digital health will be useful in evaluating and conceptualizing IQ issues associated with digital health, thus forestalling potential patient safety problems.

Trial Registration
PROSPERO International Prospective Register of Systematic Reviews CRD42018097142; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=97142

International Registered Report Identifier (IRRID)
RR2-10.1136/bmjopen-2018-024722
Associations Between Health Literacy, eHealth Literacy, and COVID-19–Related Health Behaviors Among Chinese College Students: Cross-sectional Online Study
Authors: Li Shaojie , Cui Guanghui , Kaminga Atipatsa Chiwanda , Cheng Sixiang , Xu Huilan 
Published: April 2021

Background
During the COVID-19 pandemic, the internet has significantly spread information, providing people with knowledge and advice about health protection regarding COVID-19. While a previous study demonstrated that health and eHealth literacy are related to COVID-19 prevention behaviors, few studies have focused on the relationship between health literacy, eHealth literacy, and COVID-19–related health behaviors. The latter includes not only preventative behaviors but also conventional health behaviors.

Objective
The objective of this study was to develop and verify a COVID-19–related health behavior questionnaire, explore its status and structure, and examine the associations between these behaviors and participants’ health literacy and eHealth literacy.

Methods
A snowball sampling method was adopted to recruit participants to complete anonymous cross-sectional questionnaire surveys online that assessed sociodemographic information, self-reported coronavirus knowledge, health literacy, eHealth literacy, and COVID-19–related health behaviors.

Results
Of 1873 college students who were recruited, 781 (41.7%) had adequate health literacy; the mean eHealth literacy score was 30.16 (SD 6.31). The COVID-19–related health behavior questionnaire presented a two-factor structure—COVID-19–specific precautionary behaviors and conventional health behaviors—with satisfactory fit indices and internal consistency (Cronbach α=.79). The mean score of COVID-19–related health behaviors was 53.77 (SD 8.03), and scores differed significantly (P<.05) with respect to residence, college year, academic major, family economic level, self-reported health status, having a family member or friend infected with coronavirus, and health literacy level. Linear regression analysis showed that health literacy and eHealth literacy were positively associated with COVID-19–specific precautionary behaviors (βhealth literacy=.149, βeHealth literacy=.368; P<.001) and conventional health behaviors (βhealth literacy=.219, βeHealth literacy=.277; P<.001).

Conclusions
The COVID-19–related health behavior questionnaire was a valid and reliable measure for assessing health behaviors during the pandemic. College students with higher health literacy and eHealth literacy can more actively adopt COVID-19–related health behaviors. Additionally, compared to health literacy, eHealth literacy is more closely related to COVID-19–related health behaviors. Public intervention measures based on health and eHealth literacy are required to promote COVID-19–related health behaviors during the pandemic, which may be helpful to reduce the risk of COVID-19 infection among college students.
Smart Decentralization of Personal Health Records with Physician Apps and Helper Agents on Blockchain: Platform Design and Implementation Study
Authors: Kim Hyeong-Joon , Kim Hye Hyeon , Ku Hosuk , Yoo Kyung Don , Lee Suehyun , Park Ji In , Kim Hyo Jin , Kim Kyeongmin , Chung Moon Kyung , Lee Kye Hwa , Kim Ju Han 
Published: April 2021

Background
The Health Avatar Platform provides a mobile health environment with interconnected patient Avatars, physician apps, and intelligent agents (termed IoA3) for data privacy and participatory medicine; however, its fully decentralized architecture has come at the expense of decentralized data management and data provenance.

Objective
The introduction of blockchain and smart contract technologies to the legacy Health Avatar Platform with a clinical metadata registry remarkably strengthens decentralized health data integrity and immutable transaction traceability at the corresponding data-element level in a privacy-preserving fashion. A crypto-economy ecosystem was built to facilitate secure and traceable exchanges of sensitive health data.

Methods
The Health Avatar Platform decentralizes patient data in appropriate locations (ie, on patients’ smartphones and on physicians’ smart devices). We implemented an Ethereum-based hash chain for all transactions and smart contract–based processes to guarantee decentralized data integrity and to generate block data containing transaction metadata on-chain. Parameters of all types of data communications were enumerated and incorporated into 3 smart contracts, in this case, a health data transaction manager, a transaction status manager, and an application programming interface transaction manager. The actual decentralized health data are managed in an off-chain manner on appropriate smart devices and authenticated by hashed metadata on-chain.

Results
Metadata of each data transaction are captured in a Health Avatar Platform blockchain node by the smart contracts. We provide workflow diagrams each of the 3 use cases of data push (from a physician app or an intelligent agents to a patient Avatar), data pull (request to a patient Avatar by other entities), and data backup transactions. Each transaction can be finely managed at the corresponding data-element level rather than at the resource or document levels. Hash-chained metadata support data element–level verification of data integrity in subsequent transactions. Smart contracts can incentivize transactions for data sharing and intelligent digital health care services.

Conclusions
Health Avatar Platform and interconnected patient Avatars, physician apps, and intelligent agents provide a decentralized blockchain ecosystem for health data that enables trusted and finely tuned data sharing and facilitates health value-creating transactions with smart contracts.
A Wearable Ballistocardiography Device for Estimating Heart Rate During Positive Airway Pressure Therapy: Investigational Study Among the General Population
Authors: Gardner Mark , Randhawa Sharmil , Malouf Gordon , Reynolds Karen 
Published: April 2021

Background
Obstructive sleep apnea (OSA) is a condition in which a person’s airway is obstructed during sleep, thus disturbing their sleep. People with OSA are at a higher risk of developing heart problems. OSA is commonly treated with a positive airway pressure (PAP) therapy device, which is used during sleep. The PAP therapy setup provides a good opportunity to monitor the heart health of people with OSA, but no simple, low-cost method is available for the PAP therapy device to monitor heart rate (HR).

Objective
This study aims to develop a simple, low-cost device to monitor the HR of people with OSA during PAP therapy. This device was then tested on a small group of participants to investigate the feasibility of the device.

Methods
A low-cost and simple device to monitor HR was created by attaching a gyroscope to a PAP mask, thus integrating HR monitoring into PAP therapy. The gyroscope signals were then analyzed to detect heartbeats, and a Kalman filter was used to produce a more accurate and consistent HR signal. In this study, 19 participants wore the modified PAP mask while the mask was connected to a PAP device. Participants lay in 3 common sleeping positions and then underwent 2 different PAP therapy modes to determine if these affected the accuracy of the HR estimation.

Results
Before the PAP device was turned on, the median HR error was <5 beats per minute, although the HR estimation error increased when participants lay on their side compared with when participants lay on their back. Using the different PAP therapy modes did not significantly increase the HR error.

Conclusions
These results show that monitoring HR from gyroscope signals in a PAP mask is possible during PAP therapy for different sleeping positions and PAP therapy modes, suggesting that long-term HR monitoring of OSA during PAP therapy may be possible.
Fast Healthcare Interoperability Resources (FHIR)–Based Quality Information Exchange for Clinical Next-Generation Sequencing Genomic Testing: Implementation Study
Authors: Seong Donghyeong , Jung Sungwon , Bae Sungchul , Chung Jongsuk , Son Dae-Soon , Yi Byoung-Kee 
Published: April 2021

Background
Next-generation sequencing (NGS) technology has been rapidly adopted in clinical practice, with the scope extended to early diagnosis, disease classification, and treatment planning. As the number of requests for NGS genomic testing increases, substantial efforts have been made to deliver the testing results clearly and unambiguously. For the legitimacy of clinical NGS genomic testing, quality information from the process of producing genomic data should be included within the results. However, most reports provide insufficient quality information to confirm the reliability of genomic testing owing to the complexity of the NGS process.

Objective
The goal of this study was to develop a Fast Healthcare Interoperability Resources (FHIR)–based web app, NGS Quality Reporting (NGS-QR), to report and manage the quality of the information obtained from clinical NGS genomic tests.

Methods
We defined data elements for the exchange of quality information from clinical NGS genomic tests, and profiled a FHIR genomic resource to enable information exchange in a standardized format. We then developed the FHIR-based web app and FHIR server to exchange quality information, along with statistical analysis tools implemented with the R Shiny server.

Results
Approximately 1000 experimental data entries collected from the targeted sequencing pipeline CancerSCAN designed by Samsung Medical Center were used to validate implementation of the NGS-QR app using real-world data. The user can share the quality information of NGS genomic testing and verify the quality status of individual samples in the overall distribution.

Conclusions
This study successfully demonstrated how quality information of clinical NGS genomic testing can be exchanged in a standardized format. As the demand for NGS genomic testing in clinical settings increases and genomic data accumulate, quality information can be used as reference material to improve the quality of testing. This app could also motivate laboratories to perform diagnostic tests to provide high-quality genomic data.
Deep Convolutional Neural Network–Based Computer-Aided Detection System for COVID-19 Using Multiple Lung Scans: Design and Implementation Study
Authors: Ghaderzadeh Mustafa , Asadi Farkhondeh , Jafari Ramezan , Bashash Davood , Abolghasemi Hassan , Aria Mehrad 
Published: April 2021

Background
Owing to the COVID-19 pandemic and the imminent collapse of health care systems following the exhaustion of financial, hospital, and medicinal resources, the World Health Organization changed the alert level of the COVID-19 pandemic from high to very high. Meanwhile, more cost-effective and precise COVID-19 detection methods are being preferred worldwide.

Objective
Machine vision–based COVID-19 detection methods, especially deep learning as a diagnostic method in the early stages of the pandemic, have been assigned great importance during the pandemic. This study aimed to design a highly efficient computer-aided detection (CAD) system for COVID-19 by using a neural search architecture network (NASNet)–based algorithm.

Methods
NASNet, a state-of-the-art pretrained convolutional neural network for image feature extraction, was adopted to identify patients with COVID-19 in their early stages of the disease. A local data set, comprising 10,153 computed tomography scans of 190 patients with and 59 without COVID-19 was used.

Results
After fitting on the training data set, hyperparameter tuning, and topological alterations of the classifier block, the proposed NASNet-based model was evaluated on the test data set and yielded remarkable results. The proposed model's performance achieved a detection sensitivity, specificity, and accuracy of 0.999, 0.986, and 0.996, respectively.

Conclusions
The proposed model achieved acceptable results in the categorization of 2 data classes. Therefore, a CAD system was designed on the basis of this model for COVID-19 detection using multiple lung computed tomography scans. The system differentiated all COVID-19 cases from non–COVID-19 ones without any error in the application phase. Overall, the proposed deep learning–based CAD system can greatly help radiologists detect COVID-19 in its early stages. During the COVID-19 pandemic, the use of a CAD system as a screening tool would accelerate disease detection and prevent the loss of health care resources.
Feasibility, Acceptability, and Efficacy of Virtual Reality Training for Older Adults and People With Disabilities: Single-Arm Pre-Post Study
Authors: Chau Pui Hing , Kwok Yan Yan Jojo , Chan Mee Kie Maggie , Kwan Ka Yu Daniel , Wong Kam Lun , Tang Ying Ho , Chau Kan Lung Peter , Lau Sheung Wa Matthew , Yiu Yan Yan Yannex , Kwong Mei Yan Fanny , Lai Wai Ting Thomas , Leung Mun Kit 
Published: April 2021

Background
Unlike most virtual reality (VR) training programs that are targeted at homogenous populations, a set of VR games for rehabilitation purposes targeted at a heterogeneous group of users was developed. The VR games covered physical training, cognitive training (classification and reality orientation), community-living skills training, and relaxing scenery experiences. Special considerations for local older adults and people with disabilities were made in terms of hardware choice and software design.

Objective
This study aimed to evaluate the feasibility, acceptance, and efficacy of VR training among users with varying abilities.

Methods
A single-arm pretest-posttest evaluation study was conducted. The participants of the evaluation study were encouraged to undergo 30-minute VR training three times a week for 6 weeks. The 30-minute session consisted of 10 minutes of upper-limb motion games, 10 minutes of lower-limb motion games, and 10 minutes of cognitive games/community-living skills training/relaxing scenery experiences, as appropriate. On completion of each session, usage statistics were documented via the built-in VR software, whereas feedback on the experience of the VR games and adverse events was collected via self-reports and staff observations. Feasibility was reflected by usage statistics, and acceptance was reflected by positive feedback. In addition, health outcomes, including upper-limb dexterity, functional mobility, cognitive function, and happiness, were assessed at baseline, as well as 6 weeks and 3 months after baseline. The primary outcomes were upper-limb dexterity and acceptance of playing VR games.

Results
A total of 135 participants with a mean age of 62.7 years (SD 21.5) were recruited from May 2019 to January 2020, and 124 (91.9%) completed at least one follow-up. Additionally, 76.3% (103/135) of the participants could attend at least 70% of the proposed 18 sessions, and 72.5% (1382/1906) of the sessions had a training time of at least 20 minutes. Linear mixed effect models showed statistically significant effects in terms of upper-limb dexterity (small effect) and cognitive function (moderate effect). Among the 135 participants, 88 provided positive comments. Additionally, 10.4% (14/135) reported mild discomfort, such as dizziness, and none reported severe discomfort.

Conclusions
A set of VR training games for rehabilitation could be applied to users with heterogeneous abilities. Our VR games were acceptable to local older adults and those with different disabilities. Benefits in upper-limb dexterity and cognitive function were observed despite partial compliance to the training protocol. Service providers could refer to our experiences when developing VR training systems for their clients.
Brug dog kataloget!
Authors: Redaktionen Revy- 
Published: April 2021

I Aarhus Amtstidende lørdag den 23. januar 1900 kunne man blandt andet læse om drøftelser om bygning af ”en videnskabelig højskole i Aarhus”, telegrammer fra Boerkrigen i Sydafrika og et friskt interview med den russiske forfatter, grev Leo Tolstoj. Men dagens avis indeholder også et fint læserbrev fra en trofast bruger af det daværende Landbohøjskolens Bibliotek, som i 1898 havde svimlende 2.633 udlån! Og så får vi slået fast, hvordan man bedrev litteratursøgning før internettet: Man brugte da kataloget!
Aarhus Amtstidende udkom for sidste gang i 1965.
Predictive value of d-dimer/albumin ratio and fibrinogen/albumin ratio for in-hospital mortality in patients with COVID-19
Authors: Küçükceran Kadir , Ayrancı Mustafa Kürşat , Girisgin Sadik , Koçak Sedat 
Published: April 2021

Introduction: Due to the high mortality of coronavirus disease
2019 (COVID-19), there are difficulties in the managing emergency
department. We investigated whether the d-dimer/albumin ratio (DAR) and
fibrinogen/albumin ratio (FAR) predicts mortality in the COVID-19
patients.
Methods: A total of 717 COVID-19 patients who were brought to
the emergency department from March to October 2020 were included in the
study. Levels of d-dimer, fibrinogen, and albumin, as well as DAR, FAR,
age, gender, and in-hospital mortality status of the patients were
recorded. The patients were grouped by in-hospital mortality.
Statistical comparison was conducted between the groups.
Results: Of the patients included in the study, 371(51.7%)
were male, and their median age was 64 years (50–74). There was
in-hospital mortality in 126 (17.6%) patients. The area-under-the-curve
(AUC) and odds ratio values obtained by DAR to predict in-hospital
mortality were higher than the values obtained by the all other
parameters (AUC of DAR, albumin, d-dimer, FAR, and fibrinogen: 0.773,
0.766, 0.757, 0.703, and 0.637, respectively; odds ratio of
DAR>56.36, albumin<4.015,
d-dimer>292.5, FAR>112.33, and
fibrinogen>423: 7.898, 6.216, 6.058, 4.437, and 2.794,
respectively). In addition; patients with concurrent
DAR>56.36 and FAR>112.33 had an odds ratio of
21.879 with respect to patients with concurrent DAR<56.36 and
FAR<112.33.
Conclusion: DAR may be used as a new marker to predict
mortality in COVID-19 patients. In addition the concurrent high DARs and
FARs were found to be more valuable in predicting in-hospital mortality
than either separately.
Keywords: Covid-19, D-dimer, Fibrinogen, Serum Albumin,
in-Hospital Mortality