bluuebunny/crossref_metadata_2025_split huggingface dataset:
Online education and improvement of caring behaviors of nurses in pediatric wards: a quasi-experimental study
Authors: Monemi Elnaz , Nematollahi Monirsadat 
Published: May 2023

Abstract
Background
Nurses working in pediatric wards should communicate with children well and have appropriate caring behaviors according to the age of their patients, so distance education is very suitable for them due to its availability. This study aimed to determine the effect of online education on the principles of pediatric nursing care on the caring behaviors of nurses working in pediatric wards.

Methods
This interventional (quasi-experimental) study used a simple random method to select 70 nurses working in pediatric wards and pediatric intensive care units in Kerman. The nurses in the intervention group received online training in the sky room three days a week, while nurses in the control group received routine pediatric care. The study instruments were the demographic information questionnaire and the Caring behaviors Questionnaire, which were completed by two groups before and one month after the intervention. Data were analyzed with SPSS 25. The significance level was set at P < 0.05.

Results
The independent samples t-test indicated no significant differences in the mean scores of care behaviors between the intervention (256.61 ± 5.16) and control groups (257.52 ± 3.99) before the intervention (P = 0.23) but indicated a significant difference in the mean scores of caring behaviors between the intervention (275.69 ± 6.52) and control groups (254.21 ± 3.15) after the intervention. Therefore, online education increased the score of caring behaviors in the intervention group.

Conclusion
Distance education had an impact on the caring behaviors of nurses in pediatric wards and we recommend the use of e-learning to improve the caring behaviors and the quality of care of nurses.
Community engagement processes in low- and middle-income countries health research settings: a systematic review of the literature
Authors: Mthembu Zinhle , Mogaka John J. O. , Chimbari Moses J. 
Published: May 2023

AbstractBackgroundCommunity Engagement is an important ethical imperative in research. Although substantial research emphasizes its real value and strategic importance, much of the available literature focuses primarily on the success of community participation, with little emphasis given to specific community engagement processes, mechanisms and strategies in relation to intended outcomes in research environments. The systematic literature review’s objective was to explore the nature of community engagement processes, strategies and approaches in health research settings in low- and middle-income countries.MethodsThe systematic literature review design was informed by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We searched for peer-reviewed, English-language literature published between January 2011 and December 2021 through three databases on the internet (PubMed, Web of Science and Google Scholar). The terms "community engagement," "community involvement," "participation," "research settings," and "low- and middle-income countries" were merged in the search.ResultsThe majority of publications [8/10] were led by authors from low- and middle-income countries, with many of them, [9/10] failing to continuously include important aspects of study quality. Even though consultation and information sessions were less participatory, articles were most likely to describe community engagement in these types of events. The articles covered a wide range of health issues, but the majority were concerned with infectious diseases such as malaria, human immunodeficiency virus, and tuberculosis, followed by studies on the environment and broader health factors. Articles were largely under-theorized.ConclusionsDespite the lack of theoretical underpinnings for various community engagement processes, strategies and approaches, community engagement in research settings was variable. Future studies should go deeper into community engagement theory, acknowledge the power dynamics underpin community engagement, and be more practical about the extent to which communities may participate.
Experiences of Assyrian refugee women seeking care for chronic pain: a qualitative study
Authors: Altun Areni , Brown Helen , Sturgiss Elizabeth , Russell Grant 
Published: May 2023

Abstract
Background
Refugee women exhibit some of the highest rates of chronic pain yet the diversity and challenges of health care systems across countries pose numerous challenges for refugee women trying to access quality health care.

Objective
We sought to explore the experiences of Assyrian refugee women seeking care for chronic pain.

Methods
Semi-structured interviews (face-to-face and virtual) were undertaken with 10 Assyrian women of refugee background living in Melbourne, Australia. Audio recordings and field notes of interviews were collected and themes were identified using a phenomenological approach. Women were required to be conversant in English or Arabic and willing to use a translator if necessary.

Results
We identified five major themes of women’s experiences accessing care for chronic pain: (1) the story of pain; (2) the experience of help seeking in Australia and home country; (3) factors shaping the ability to access appropriate care; (4) support seeking systems; and (5) influence of culture and gender roles.

Conclusion
Exploring refugee women’s experience of seeking care for chronic pain reinforces the need to explore hard to reach population’s perspectives in research and helps to understand how vectors of disadvantage may intersect. For successful integration into health care systems of host countries, particularly for complex conditions such as chronic pain, there is a need to work with women community members to develop programs that are culturally aligned to enhance access pathways to care.
Automatic detection and recognition of nasopharynx gross tumour volume (GTVnx) by deep learning for nasopharyngeal cancer radiotherapy through magnetic resonance imaging
Authors: Wang Yandan , Chen Hehe , Lin Jie , Dong Shi , Zhang Wenyi 
Published: May 2023

Abstract
Background
In this study, we propose the deep learning model-based framework to automatically delineate nasopharynx gross tumor volume (GTVnx) in MRI images.

Methods
MRI images from 200 patients were collected for training-validation and testing set. Three popular deep learning models (FCN, U-Net, Deeplabv3) are proposed to automatically delineate GTVnx. FCN was the first and simplest fully convolutional model. U-Net was proposed specifically for medical image segmentation. In Deeplabv3, the proposed Atrous Spatial Pyramid Pooling (ASPP) block, and fully connected Conditional Random Field(CRF) may improve the detection of the small scattered distributed tumor parts due to its different scale of spatial pyramid layers. The three models are compared under same fair criteria, except the learning rate set for the U-Net. Two widely applied evaluation standards, mIoU and mPA, are employed for the detection result evaluation.

Results
The extensive experiments show that the results of FCN and Deeplabv3 are promising as the benchmark of automatic nasopharyngeal cancer detection. Deeplabv3 performs best with the detection of mIoU 0.8529 ± 0.0017 and mPA 0.9103 ± 0.0039. FCN performs slightly worse in term of detection accuracy. However, both consume similar GPU memory and training time. U-Net performs obviously worst in both detection accuracy and memory consumption. Thus U-Net is not suggested for automatic GTVnx delineation.

Conclusions
The proposed framework for automatic target delineation of GTVnx in nasopharynx bring us the desirable and promising results, which could not only be labor-saving, but also make the contour evaluation more objective. This preliminary results provide us with clear directions for further study.
Surgical site infection in spinal surgery: a bibliometric analysis
Authors: Wang Xun , Lin Yanze , Yao Wenchao , Zhang Aiqi , Gao Liqing , Feng Fabo 
Published: May 2023

Abstract
Background
Surgical site infection (SSI) is a common complication in spinal surgery that can significantly affect the patient's prognosis. Despite advances in surgical techniques and infection control measures, SSI remains a considerable concern for healthcare providers and patients alike. In recent years, there has been a steady increase in studies related to SSI in spine surgery, leading to the publication of numerous informative articles. However, the current state and trends of research in the field of spinal SSI remain unclear. This study aims to conduct a bibliometric analysis of SSI-related articles in spine surgery to identify research status and trends. Meanwhile, we identify the top 100 most cited articles for further analysis.

Methods
We searched for all articles related to spinal SSI in the Web of Science Core Collection, recording the publication year, country, journal, institution, keywords, and citation frequency for further analysis. In addition, we identified and analyzed the top 100 most cited articles.

Results
A total of 307 articles related to spinal SSI were identified. All of these articles were published between 2008 and 2022, with the number of publications showing an increasing trend over the years. The related articles originated from 37 countries, with the USA contributing the most (n = 138). The institution with the highest number of publications and citations was Johns Hopkins University (14 articles; 835 citations). Among the journals, Spine had the highest number of articles (n = 47). The prevention of spinal SSI has been a research hotspot in recent years. Among the top 100 most cited articles, the most common research theme was the risk factors associated with spinal SSI.

Conclusions
In recent years, research related to spinal SSI has attracted the attention of numerous clinicians and scholars. As the first bibliometric analysis of spinal SSI, our study aims to provide pragmatic guidance for clinicians to learn the research status and trends in this field and improve their vigilance toward SSI.
Stimuli-responsive polymer-based bioinspired soft robots
Authors: Panda Swati , Hajra Sugato , Rajaitha P. Mary , Kim Hoe Joon 
Published: May 2023

AbstractSoft robotics enables various applications in certain environments where conventional rigid robotics cannot deliver the same performance due to their form factor and stiffness. Animals use their soft external organs to carry out activities in response to challenging natural environments efficiently. The objective of soft robots is to provide biologically inspired abilities and enable adaptable and flexible interactions with complex objects and surroundings. Recent advances in stimuli-responsive soft robot technology have heavily used polymer-based multifunctional materials. Soft robots with incredibly sophisticated multi-mechanical, electrical, or optical capabilities have demonstrated the ability to modify their shape intelligently in response to external stimuli, such as light, electricity, thermal gradient, and magnetic fields. This short review covers recent advances in scientific techniques for incorporating multifunctional polymeric materials into stimuli-responsive bioinspired soft robots and their applications. We also discuss how biological inspiration and environmental effects can provide a viable viewpoint for bioinspired design in the innovative field of soft robotics. Lastly, we highlight the future outlooks and prospects for soft, stimuli-responsive, bio-inspired robots.
Data infrastructures for AI in medical imaging: a report on the experiences of five EU projects
Authors: Kondylakis Haridimos , Kalokyri Varvara , Sfakianakis Stelios , Marias Kostas , Tsiknakis Manolis , Jimenez-Pastor Ana , Camacho-Ramos Eduardo , Blanquer Ignacio , Segrelles J. Damian , López-Huguet Sergio , Barelle Caroline , Kogut-Czarkowska Magdalena , Tsakou Gianna , Siopis Nikolaos , Sakellariou Zisis , Bizopoulos Paschalis , Drossou Vicky , Lalas Antonios , Votis Konstantinos , Mallol Pedro , Marti-Bonmati Luis , Alberich Leonor Cerdá , Seymour Karine , Boucher Samuel , Ciarrocchi Esther , Fromont Lauren , Rambla Jordi , Harms Alexander , Gutierrez Andrea , Starmans Martijn P. A. , Prior Fred , Gelpi Josep Ll. , Lekadir Karim 
Published: May 2023

AbstractArtificial intelligence (AI) is transforming the field of medical imaging and has the potential to bring medicine from the era of ‘sick-care’ to the era of healthcare and prevention. The development of AI requires access to large, complete, and harmonized real-world datasets, representative of the population, and disease diversity. However, to date, efforts are fragmented, based on single–institution, size-limited, and annotation-limited datasets. Available public datasets (e.g., The Cancer Imaging Archive, TCIA, USA) are limited in scope, making model generalizability really difficult. In this direction, five European Union projects are currently working on the development of big data infrastructures that will enable European, ethically and General Data Protection Regulation-compliant, quality-controlled, cancer-related, medical imaging platforms, in which both large-scale data and AI algorithms will coexist. The vision is to create sustainable AI cloud-based platforms for the development, implementation, verification, and validation of trustable, usable, and reliable AI models for addressing specific unmet needs regarding cancer care provision. In this paper, we present an overview of the development efforts highlighting challenges and approaches selected providing valuable feedback to future attempts in the area.Key points• Artificial intelligence models for health imaging require access to large amounts of harmonized imaging data and metadata.• Main infrastructures adopted either collect centrally anonymized data or enable access to pseudonymized distributed data.• Developing a common data model for storing all relevant information is a challenge.• Trust of data providers in data sharing initiatives is essential.• An online European Union meta-tool-repository is a necessity minimizing effort duplication for the various projects in the area.
Hemostasis assessment in Fontan patients using the new thrombodynamics test
Authors: Minaev Anton V. , Gushchin Dmitry K. , Kovalev Dmitry V. , Mwela Bupe M. 
Published: May 2023

Abstract
Background
Thromboembolic events are a well-known risk for Fontan patients and often lead to morbidity and mortality in cyanotic patients and patients with a single ventricle. Coagulopathy and thrombophilia, in addition to disturbed Fontan blood flow and endothelial injury, are major contributors to thromboembolic complications. However, there is currently no consensus regarding the optimal medication to prevent or treat these events. Identification of coagulation disorders is therefore crucial for selecting appropriate management strategies and evaluating long-term outcomes in these patients.

Case presentation
We present the case of a 35-year-old male who underwent the Fontan procedure with a total cavopulmonary modification during childhood due to tricuspid atresia. He was admitted with complaints of headaches and visual disturbances, but no clear cardiovascular cause was identified. Standard coagulation test parameters were normal, but the thrombodynamics test indicated severe hypercoagulation and spontaneous clot formation. Anticoagulation therapy was initiated, and a subsequent thrombodynamics assay showed normalization of the coagulation parameters. The patient remained asymptomatic during the six-month follow-up period.

Conclusions
The thrombodynamics test is a valuable tool for the diagnosis of coagulation disorders, as it can assess coagulation parameters and clot growth in vitro. This method can also aid in the optimization of antithrombotic therapy. The presented clinical case highlights the potential use of the thrombodynamics test in Fontan patients to diagnose coagulation disorders and improve long-term outcomes.
Biochemical Markers of Bone Fragility in Patients With Diabetes
Authors: Meier Christian , Eastell Richard , Pierroz Dominique D , Lane Nancy E , Al-Daghri Nasser , Suzuki Atsushi , Napoli Nicola , Mithal Ambrish , Chakhtoura Marlene , Fuleihan Ghada El-Hajj , Ferrari Serge 
Published: May 2023

AbstractContextThe risk of fragility fractures is increased in both type 1 and type 2 diabetes. Numerous biochemical markers reflecting bone and/or glucose metabolism have been evaluated in this context.ObjectiveThis review summarizes current data on biochemical markers in relation to bone fragility and fracture risk in diabetes.MethodsA group of experts from the International Osteoporosis Foundation and European Calcified Tissue Society reviewed the literature focusing on biochemical markers, diabetes, diabetes treatments, and bone in adults.ResultsAlthough bone resorption and bone formation markers are low and poorly predictive of fracture risk in diabetes, osteoporosis drugs seem to change bone turnover markers (BTMs) in diabetics similarly to nondiabetics, with similar reductions in fracture risk. Several other biochemical markers related to bone and glucose metabolism have been correlated with bone mineral density and/or fracture risk in diabetes, including osteocyte-related markers such as sclerostin, glycated hemoglobin A1c (HbA1c) and advanced glycation end products, inflammatory markers, and adipokines, as well as insulin-like growth factor-1 and calciotropic hormones.ConclusionSeveral biochemical markers and hormonal levels related to bone and/or glucose metabolism have been associated with skeletal parameters in diabetes. Currently, only HbA1c levels seem to provide a reliable estimate of fracture risk, while BTMs could be used to monitor the effects of antiosteoporosis therapy.
MCNP simulations for the n_TOF NEAR station
Authors: Kopanos Sotiris Alexandros 
Published: May 2023

The NEAR station is the newest experimental area of the n_TOF facility at CERN, utilizing the spallation process to generate extremely high neutron flux within a broad energy spectrum. As part of the campaign for the determination of the neutron beam spectral features, numerous foils have been irradiated last year and the induced activities were measured implementing a HPGe detector.
Experimental data analysis of the irradiated foil’s gamma-ray spectra, yielded the saturated activities for each material that can be used to unfold the characteristics of the neutron flux through data deconvolution using the SAND-II code. Since this process is mostly mathematical and does not account for geometry, shielding and scattering effects, correction factors need to be defined and applied to the measured activities in order to produce a more robust and accurate result.
For this purpose Monte Carlo simulations using the MCNP code were performed to investigate the self-shielding of the foils and its effect on both the neutron flux and the gamma rays emitted. Also, a detailed simulation including the full geometry of the experimental setup was developed and used in order to investigate possible influence of the peripheral materials (cement, other foils, mylar as well as the aluminum sample holder and rails) to the total flux that reaches each individual foil through scattering of neutrons.
Study of the cross section biasing technique using GEANT4 and determination of the parasitic neutrons at N.C.S.R. “Demokritos”
Authors: Ziagkova Anastasia , Michael Axiotis  , Sotirios Chasapoglou  , Maria Diakaki  , Kalliopi Kaperoni  , Michael Kokkiris  , Anastasios Lagoyannis  , Veatriki Michalopoulou  , Roza Vlastou  
Published: May 2023

A simulation code was developed using the GEANT4 [1] toolkit in order to determine the behavior of the neutron production beams generated by proton induced reactions while applying the cross section biasing technique [2]. As the application of the biasing technique can cause a change in the physical processes occurring during the simulation, the specific implemented technique was tested via control simulations to determine any deviations of the results from the theoretically expected ones. Different materials, geometries and biasing factors were used in order to qualify and quantify the discrepancies between the unbiased and the biased simulations.
One of the main reactions used for the production of the neutron beam at the Tandem accelerator laboratory of N.C.S.R. “Demokritos” [3] is the 3H(p,n)3He one. In the geometry of the main tritiated target, elements such as molybdenum, copper and titanium are included. During the interaction of the proton beam with them, it is possible to produce neutrons that will “contaminate” the main neutron beam. These neutrons are called parasitic and their quantification is necessary in order to avoid obtaining erroneous results in cross section measurements on the various targets under study.[4] By constructing the proper geometry and using the GEANT4 code mentioned above, the determination of the parasitic neutrons is achieved with optimal statistical results in short computational times, while the discrepancies between the unbiased and the biased results remain minimal.
Taming the Long Tail: The Gambler’s Fallacy in Intermittent Demand Management
Authors: Bi Sheng , He Long , Teo Chung Piaw 
Published: May 2023

Problem definition: “Long tail” products with intermittent demand often tie up valuable warehouse space and capital investment for many companies. Furthermore, the paucity of demand data poses additional challenges for model estimation and performance evaluation. Traditional inventory solutions are not designed for products with intermittent demand. In this paper, we propose a new framework to optimize the choice of “replenishment timing” and “replenishment quantity” for managing the inventory metrics of long tail products, when evaluated over a finite horizon. Methodology/results: Our analysis is motivated by a recent interesting observation that the gambler’s fallacy phenomenon actually holds in a finite number of coin tosses. We use this phenomenon to analyze the inventory problem for intermittent demand to demonstrate that classical inventory models using KPIs such as fill rate, average cost per cycle, or average cost per unit, etc., must necessarily “bias” the underlying demand distribution to account for the finite horizon effect. We provide the exact closed-form expressions of the biased distribution to account for this effect in performance evaluation. The results show that the choice of replenishment timing and replenishment quantity is essential to superior performance on several key inventory metrics. Managerial implications: For long tail products, the belief that it is less likely for another demand to arrive shortly after a preceding one (the gambler’s fallacy), turns out to be true when performance is tabulated over a finite horizon, even if demands across time are independent. So it pays to delay the replenishment of depleted stocks to save on holding cost and warehouse space. Managers can optimize the replenishment timing, besides choosing the replenishment quantity, to optimize the performance metrics of several classes of inventory problems. This is especially useful for companies managing a large number of long tail products. Funding: This work was supported in part by the National Natural Science Foundation of China [Grant 72192832] and the 2019 Academic Research Fund Tier 3 of the Ministry of Education-Singapore [Grant MOE-2019-T3-1-010]. Supplemental Material: The e-companion is available at https://doi.org/10.1287/msom.2023.1217 .