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Although in-feed advertising is popular on mainstream platforms, academic research on it is limited. Platforms typically deliver organic content through two methods: subscription by users or recommendation by artificial intelligence. However, little is known about the ad performance between these two channels. This research examines how the performance of in-feed ads, in terms of click-through rates and conversion rates, differs between subscription and recommendation channels and whether these effects are mediated by ad intrusiveness and moderated by ad attributes. Two ad attributes are investigated: ad appeal (informational vs. emotional) and ad link (direct vs. indirect). Study 1 finds that the recommendation channel generates higher click-through rates but lower conversion rates than the subscription channel, and these effects are amplified by informational ad appeal and direct ad links. Study 2 explores channel differences, revealing that the recommendation channel yields less source credibility and content control, reducing consumer engagement with organic content. Studies 3 and 4 validate the mediating role of ad intrusiveness and rule out ad recognition as an alternative explanation. Study 5 uses eye-tracking technology to show that the recommendation channel has lower content engagement, lower ad intrusiveness, and greater ad interest. [ABSTRACT FROM AUTHOR]

In the recruitment process, manually selecting suitable candidates from curriculum vitae (CVs) for a job description (JD) is both time-consuming and expensive. Traditional keyword-based methods struggle to capture skill semantics, prompting the development of more advanced JD-CV matching systems. This paper aims to investigate and construct an ontology-based skills recommendation system, with objectives including creating a skills ontology and developing skills matching methods for JD-CV pairs. The objective of our approach is to enhance the accuracy and contextual relevance of recommendations by utilizing the proposed score. The proposed skills ontology and skills matching strategies are applied to a real dataset in Vietnam. The results of our study can automatically recommend a list of CVs for a given JD. Furthermore, the findings indicate that our proposed model surpasses comparative approaches by a margin of at least 1% to 5%. Overall, the study demonstrates the potential of utilizing ontology-based approaches to offer a practical solution for enhancing hiring practices. [ABSTRACT FROM AUTHOR]

Objective Methods Results Discussion Letters of recommendation (LORs) are an important part of pain medicine fellowship applications that may be subject to implicit bias by the letter's author. This study evaluated letters of recommendation for applications to pain medicine fellowships in the United States to characterize biases and differences among applicants over four application cycles.This was a retrospective single‐site cohort study. De‐identified LORs were collected from 2020 to 2023 from one institution. The Valence Aware Dictionary and sEntiment Reasoner (VADER) natural language processing package scored positive LOR sentiment. In addition, the deep learning tool, Empath, scored LORs for 15 sentiments. Wilcoxon rank‐sum and one‐way ANOVA tests compared scores between applicant demographics: gender, race, medical school type, residency specialty, and chief resident status, as well as letter writers' academic position.Nine hundred and sixty‐four applications were studied over four application cycles. Program directors wrote fewer words (p = 0.020) and less positively (p < 0.001) compared to department chairs and letter writers with neither position. Department chairs wrote with less “negative emotion” compared to both program directors and writers with neither position (p < 0.001). Anesthesiologist applicants received more letters highlighting “achievement” (p < 0.001) while PM&R applicants submitted letters with less “negative emotion” (p < 0.001) compared to other specialties. Chief residents' letters scored higher in “leader” sentiment (p < 0.001) and lower in “negative emotion” (p < 0.001).Linguistic content did not favor certain genders or races over others. However, disparities in LORs do exist depending on an applicant's specialty and chief resident status, as well as the academic status of the letter writer. [ABSTRACT FROM AUTHOR]

The surgical reconstruction of severe corneal ulcers is a common and crucial component of the clinical practice of veterinary ophthalmology. Numerous surgical techniques are used in dogs for corneal reconstruction, and these techniques may be categorized by the material used to repair the corneal lesion. The first part of the present review described procedures that utilize autogenous ocular tissues, homologous donor tissues, and heterologous donor tissues. In this second part of the review, the categories of biomaterials and keratoprosthetics will be summarized. Biomaterials that are reported for use in dogs include amniotic membrane, porcine urinary bladder acellular matrix, porcine small intestinal submucosa, acellular porcine corneal stroma, and other miscellaneous soft tissue and cartilage grafts (e.g., preserved equine renal capsule, autologous omentum, autologous buccal mucosa membrane, bovine pericardium, and homologous peritoneum). Descriptions of keratoprosthesis surgery in dogs are currently limited, but the use of artificial corneal transplants hold promise for dogs with severe, vision‐compromising corneal disease that is not amenable to other reconstruction techniques. This review describes the results of experimental studies evaluating these graft materials in dogs, and it will summarize the findings and outcomes of the clinical articles published in each material category. Reporting inconsistencies and areas where additional research is required will be highlighted to help guide future studies in this area. A major aim of this review is to help identify potential subjects that could be evaluated in future investigations and that might lead to refinements in clinical practice. [ABSTRACT FROM AUTHOR]

Fertilizer recommendation is the crucial factor to be considered in automation of agricultural predictions. Fertilizer fill the necessary portion of any farming region. There are some micronutrients and macro nutrients which need to be given to crops for proper growth. If fertilization is not done to an optimum level, it may badly harm the soil quality and crop health, so optimum fertilization is important. In this paper we discuss fertilizer and nutrient recommender, where we have used a new feature selection methodology. We have shown the difference between two implementation cases considering presence and absence of feature ranking and selection. Feature ranking and selection has clearly increased the efficiency of the fertilizer & nutrient recommender in our work from 85% to 98%. Feature selection & raking has been introduced with random forest approach. [ABSTRACT FROM AUTHOR]

Exploring the potential of technology in addressing Sustainable Development Goals (SDGs) within intricate rural contexts holds paramount significance. Sustainable rural development holds profound significance for both developed and developing nations. This study was conducted to develop a methodological framework for placing experts with strategically relevant competencies to meet the specific needs of villages, thus enabling the practical application of their expertise in generating innovative solutions to problems in a village. This study entails several pivotal phases. Firstly, it constructs a Community Standard of Living Index (CSLI) using Delphi and Rank Reciprocal (RR). Secondly, it establishes village clustering through a hybrid Fuzzy CMeans (FCM), Self-Organizing Map (SOM), and Xie-Beni (XB). Thirdly, a classification of village development levels is created using Tsukamoto and Smallest of Maximum (SM). Finally, recommendations for placing experts in villages, aligning their skills with identified needs using the Cosine Similarity (CS). The results obtained are compared with factual data of each village to obtain relevant conclusions, where an accuracy value of 0.95 indicates a high success rate in the test results of the proposed technique. This study has the potential to significantly enhance decision-making by introducing opportunities for the development of hybrid methodologies in expert mapping for rural issues. [ABSTRACT FROM AUTHOR]

Graph convolutional networks (GCNs) have become prevalent in recommender system (RS) due to their superiority in modeling collaborative patterns. Although improving the overall accuracy, GCNs unfortunately amplify popularity bias — tail items are less likely to be recommended. This effect prevents the GCN-based RS from making precise and fair recommendations, decreasing the effectiveness of recommender systems in the long run. In this paper, we investigate how graph convolutions amplify the popularity bias in RS. Through theoretical analyses, we identify two fundamental factors: (1) with graph convolution (i.e., neighborhood aggregation), popular items exert larger influence than tail items on neighbor users, making the users move towards popular items in the representation space; (2) after multiple times of graph convolution, popular items would affect more high-order neighbors and become more influential. The two points make popular items get closer to almost users and thus being recommended more frequently. To rectify this, we propose to estimate the amplified effect of popular nodes on each node’s representation, and intervene the effect after each graph convolution. Specifically, we adopt clustering to discover highly-influential nodes and estimate the amplification effect of each node, then remove the effect from the node embeddings at each graph convolution layer. Our method is simple and generic — it can be used in the inference stage to correct existing models rather than training a new model from scratch, and can be applied to various GCN models. We demonstrate our method on two representative GCN backbones LightGCN and UltraGCN, verifying its ability in improving the recommendations of tail items without sacrificing the performance of popular items. Codes are open-sourced 1). [ABSTRACT FROM AUTHOR]

The incremental recommendation involves updating existing models by extracting information from interaction data at current time-step, with the aim of maintaining model accuracy while addressing limitations including parameter dependencies and inefficient training. However, real-time user interaction data is often afflicted by substantial noise and invalid samples, presenting the following key challenges for incremental model updating: (1) how to effectively extract valuable new knowledge from interaction data at the current time-step to ensure model accuracy and timeliness, and (2) how to safeguard against the catastrophic forgetting of long-term stable preference information, thus preserving the model's sensitivity during cold-starts. In response to these challenges, we propose the Incremental Recommendation with Stable Latent Side-information Updating (SIIFR). This model employs a side-information augmenter to extract valuable latent side-information from user interaction behavior at time-step T, thereby sidestepping the interference caused by noisy interaction data and acquiring stable user preference. Moreover, the model utilizes rough interaction data at time-step T + 1 , in conjunction with existing side-information enhancements to achieve incremental updates of latent preferences, thereby ensuring the model's efficacy during cold-start. Furthermore, SIIFR leverages the change rate in user latent side-information to mitigate catastrophic forgetting that results in the loss of long-term stable preference information. The effectiveness of the proposed model is validated and compared against existing models using four popular incremental datasets. The model code can be achieved at: https://github.com/LNNU-computer-research-526/FR-sii. [ABSTRACT FROM AUTHOR]

With the rapid popularity of online social media, recommendation systems have increasingly harnessed social relations to enhance user-item interactions and mitigate the data sparsity issue. Beyond social connections, the semantic relatedness among items has emerged as a crucial factor in comprehending their inherent connections. In this work, we propose a novel Multi-view Contrastive learning framework for Social Recommendation, named MultiCSR. This framework adaptively incorporates user social networks and item knowledge graphs into modeling users preferences within recommendation systems. To facilitate the alignment of different views, we introduce a dedicated multi-view contrastive learning process that extracts rich information from each view and foster mutual enhancement. Extensive experiments conducted on three real-world datasets demonstrate the effectiveness of our framework over representative recommendation methods. Furthermore, ablation studies offer a deeper understanding of the mechanisms underlying our framework. [ABSTRACT FROM AUTHOR]

Prevalence of heart failure (HF) and diabetes are markedly increasing globally. In a population of HF patients, approximately 40% have diabetes which is associated with a more severe HF, poorer cardiovascular outcomes and higher hospitalization rates for HF than HF patients without diabetes. Similar trends were shown in HF patients with prediabetes. In addition, the association between HF and renal function decline was demonstrated in patients with or without diabetes. However, the exact prevalence of dysglycemia in HF patients requires further investigation aiming to clarify the most accurate test to detect dysglycemia in this population. The relationship between HF and diabetes is complex and probably bidirectional. In one way, patients with diabetes have a more than two-fold risk of developing incident HF with reduced or preserved ejection fraction than those without diabetes. In the other way, patients with HF, when compared with those without HF, show an increased risk for the onset of diabetes due to several mechanisms including insulin resistance (IR), which makes HF emerging as a precursor for diabetes development. This article provides epidemiological evidence of undetected dysglycemia (prediabetes or diabetes) in HF patients and reviews the pathophysiological mechanisms which favor the development of IR and the risks associated with these disorders in HF patients. This review also offers a discussion of various strategies for the prevention of diabetes in HF patients, based first on fasting plasma glucose and HbA1c measurement and if normal on an oral glucose tolerance test as diagnostic tools for prediabetes and unknown diabetes that should be performed more extensively in those patients. It discusses the implementation of diabetes prevention measures and well-structured management programs for HF patients who are generally overweight or obese, as well as current pharmacotherapeutic options for prediabetes, including sodium–glucose cotransporter 2 inhibitors which are among the pillars of HF treatment and which recently showed a benefit in the reduction of incident diabetes in HF patients. Thus, there is an urgent need of routine screening for dysglycemia in all HF patients, which should contribute to reduce the incidence of diabetes and to treat earlier diabetes when already present. [ABSTRACT FROM AUTHOR]

Ibrutinib, a highly effective inhibitor of the Bruton tyrosine kinase, has significantly transformed the therapeutic approach in chronic lymphocytic leukemia (CLL). Despite these advancements, the disease continues to be characterized by immune dysfunction and increased susceptibility to infections, with mortality rates from infections showing no significant improvement over the past few decades. Therefore, timely prevention, recognition, and treatment of infections remains an important aspect of the standard management of a patient with CLL. A panel of hematologists with expertise in CLL met to discuss existing literature and clinical insights for the management of infectious in CLL undergoing ibrutinib treatment. Despite not being a fully comprehensive review on the topic, this work provides a set of practical recommendations that can serve as a guide to healthcare professionals who manage these patients in their daily clinical practice. [ABSTRACT FROM AUTHOR]

Due to the fast-changing environments caused by artificial intelligence development, the socio-technical challenge in contemporary educational systems focuses on the need for more regulative measures guiding system stakeholders' behavior. In fulfilling the present legal gap, enacted soft law regulation has been laid out, and a detailed systematic literature review was conducted in the paper presented. The specific methodological approach was selected to deal with two crucial research tasks: to reveal and recommend fundamental governing mechanisms regarding the use and application of generative artificial intelligence; more precisely, large language models in educational systems. Three systematically guided layers of quantitative and qualitative content analysis of central policy, legislation, and regulatory mechanisms in governing AI in education were extracted from the 142 Scopus Database and Web of Science research papers analyzed and presented. These research findings benefit policymakers, regulatory and legislative bodies, and agencies in constructing governing frames for using and applying generative artificial intelligence in education. [ABSTRACT FROM AUTHOR]

By means of visiting folic acid processing plant, this article introduces the structure, metabolism and mechanism of folic acid, and the recommendations for folic acid supplementation are given. In order to help those who need to take folic acid supplements, this article also provides a deep understanding of the functions of folic acid in the body by combining scientific and entertaining ways. [ABSTRACT FROM AUTHOR]

Atopic dermatitis often begins in infancy and follows a chronic course of exacerbations and remissions. The etiology is complex and involves numerous factors that contribute to skin barrier defect and inflammation. In the Middle East, the burden of atopic dermatitis is understudied. Epidemiological data specific to the Gulf region are scarce but reveal a prevalence of up to about 40% in the United Arab Emirates. Region-specific factors, such as the climate and the frequency of consanguineous marriages, may affect atopic dermatitis incidence, prevalence, and evolution over time. A panel of experts predominantly from the United Arab Emirates analyzed the evidence from published guidelines, and considered expert guidance and local treatment practices to develop clear recommendations for the management of atopic dermatitis in the United Arab Emirates. They encourage a systematic approach for the diagnosis and treatment, using disease severity scores and quality-of-life measurement tools. Treatment recommendations take into consideration both established therapies and the approved systemic biologics dupilumab and tralokinumab, and the Janus kinase inhibitors baricitinib, upadacitinib, and abrocitinib. [ABSTRACT FROM AUTHOR]

Algorithmic Impact Assessments (AIAs) are often suggested as a tool to help identify and evaluate actual and potential harms of algorithmic systems. While the existing literature on AIAs provides a valuable foundation, critical understanding gaps remain, including the lived experiences of practitioners who implement assessments and a lack of standardization across industry. Such gaps pose significant risks to the usefulness of assessments in the responsible development of algorithmic systems. By conducting 107 assessments with practitioners who build personalization, recommendation, and subscription systems at a large online audio-streaming platform and 8 semi-structured stakeholder interviews, we attempt to bridge this gap by identifying practitioners' challenges when applying AIAs that might hinder their effectiveness. The paper analyzes whether harms described in the literature related to machine learning and recommendation systems are similar to the concerns practitioners have. We find that the challenges practitioners encounter can be grouped into three categories: technical and methods, infrastructure and operations, and resourcing and prioritization. We also describe ways for teams to more effectively mitigate concerns. This paper helps bridge gaps between the theory and practice of AIAs, advances understanding of the potential harms of algorithmic systems, and informs assessment practices to serve their intended purpose. [ABSTRACT FROM AUTHOR]

Background: Mycoplasma pneumoniae (M. pneumoniae) is a significant contributor to community-acquired pneumonia among children. Since 1968, when a strain of M. pneumoniae resistant to macrolide antibiotics was initially reported in Japan, macrolide-resistant M. pneumoniae (MRMP) has been documented in many countries worldwide, with varying incidence rates. MRMP infections lead to a poor response to macrolide antibiotics, frequently resulting in prolonged fever, extended antibiotic treatment, increased hospitalization, intensive care unit admissions, and a significantly higher proportion of patients receiving glucocorticoids or second-line antibiotics. Since 2000, the global incidence of MRMP has gradually increased, especially in East Asia, which has posed a serious challenge to the treatment of M. pneumoniae infections in children and attracted widespread attention from pediatricians. However, there is still no global consensus on the diagnosis and treatment of MRMP in children. Methods: We organized 29 Chinese experts majoring in pediatric pulmonology and epidemiology to write the world's first consensus on the diagnosis and treatment of pediatric MRMP pneumonia, based on evidence collection. The evidence searches and reviews were conducted using electronic databases, including PubMed, Embase, Web of Science, CNKI, Medline, and the Cochrane Library. We used variations in terms for "macrolide-resistant", "Mycoplasma pneumoniae", "MP", "M. pneumoniae", "pneumonia", "MRMP", "lower respiratory tract infection", "Mycoplasma pneumoniae infection", "children", and "pediatric". Results: Epidemiology, pathogenesis, clinical manifestations, early identification, laboratory examination, principles of antibiotic use, application of glucocorticoids and intravenous immunoglobulin, and precautions for bronchoscopy are highlighted. Early and rapid identification of gene mutations associated with MRMP is now available by polymerase chain reaction and fluorescent probe techniques in respiratory specimens. Although the resistance rate to macrolide remains high, it is fortunate that M. pneumoniae still maintains good in vitro sensitivity to second-line antibiotics such as tetracyclines and quinolones, making them an effective treatment option for patients with initial treatment failure caused by macrolide antibiotics. Conclusions: This consensus, based on international and national scientific evidence, provides scientific guidance for the diagnosis and treatment of MRMP in children. Further studies on tetracycline and quinolone drugs in children are urgently needed to evaluate their effects on the growth and development. Additionally, developing an antibiotic rotation treatment strategy is necessary to reduce the prevalence of MRMP strains. [ABSTRACT FROM AUTHOR]

The goal of the study was to assess in-school adolescents’ awareness of WHO physical activity recommendations for adolescents and to determine differences in their awareness based on school location and school type. The study used descriptive cross-sectional survey research design. Population was made up of 7740 junior and senior secondary school students in Nuskka local government area (LGA) Enugu state. Instrument for data collection was questionnaire. Data were analysed using frequencies, percentages, and Chi-square test applied at the .05 level of significance. Findings show that adolescents possess low level of awareness of PA recommendations. Adolescents in urban and rural locations, as well as those in single sex and mixed sex schools also possess low level of PA recommendations. The study revealed no significant differences in awareness of PA recommendation for adolescents based on school location but, significant difference existed between school types. The study discussed the implications of adolescents’ low level of awareness of PA guidelines to optimal health as well as the health cost impact on the families. To encourage adolescent PA engagement, use of multiple intervention strategies such as incorporating PA guidelines into the school curriculum, use of mass media campaign and community workshops that would cover both rural and urban schools were recommended for the Ministry of education and school administrators. [ABSTRACT FROM AUTHOR]

Recommendation has become especially crucial during the COVID-19 pandemic as a significant number of people rely on online shopping from home. Existing recommendation algorithms, designed to address issues like cold start and data sparsity, often overlook the time constraints of users. Specifically, users expect to receive recommendations for products of interest in the shortest possible time. To address this challenge, we propose a novel collaborative filtering recommendation algorithm that leverages the advantages of quantum computing circuits based on data reconstruction. This approach allows for the rapid identification of users similar to the target user, thereby improving recommendation speed. In our method, we utilize the information of known users to linearly reconstruct that of the target users, forming a relational matrix. Subsequently, we employ \(l_{2,1}-\) norm and \(l_{1}-\) norm to sparsely constrain the relationship matrix, deducing the weight of each known user. The final step involves providing similar recommendations to target users based on these weights. Furthermore, we implement the proposed algorithm using a quantum circuit, enabling exponential acceleration. The final weight matrix is derived from the quantum state outputted by the circuit. The speed of this process is theoretically demonstrated in detail. Experimental results indicate that our algorithm outperforms state-of-the-art methods in terms of root mean squared error (RMSE), mean absolute error (MAE) and normalized discounted cumulative gain (NDCG). Compared to state-of-the-art comparison algorithms, the proposed algorithm achieves the fastest recommendation speed across eight public datasets. [ABSTRACT FROM AUTHOR]

Fertilizer recommendations for phosphorus (P) are generally based on the soil content of plant available P and the amount of P exported by the harvested crop. In countries of the former Soviet Union (SU), a widespread method is the Machigin extraction. The relationships between soil P contents extracted with this and other European methods are not well established. Here, we compare the Machigin soil P extraction method with the Calcium Ammonium Lactate P(CAL) procedure that is commonly used in Central Europe. Both methods were tested with 94 soil samples originating from Kyrgyzstan, Ukraine, and Germany. Overall, the P amount extracted with the CAL method was 2.8 times higher than that extracted with the Machigin method. However, using results from all analyzed individual soil samples, the relation between P(CAL) and P(Machigin) showed large scatter and an R2 of only 0.364. Differing relations between P(CAL) and P(Machigin) were observed for soils with pH >7 and <7. However, these are overshadowed by differences between the three geographic groups of the soil samples. The estimated soil P fertility classes for the Machigin extraction were mostly higher than for the German (CAL) procedure, resulting in lower fertilizer recommendations for the Machigin method. To improve the accuracy of conversion between both methods, evaluation of laboratory proficiency tests (i.e. analysis of the same soil samples in different laboratories with the same methods) of the Machigin method should be useful. [ABSTRACT FROM AUTHOR]

Background: Recent publications underscore the need for updated recommendations addressing less radical surgery for <2 cm tumors, induction chemotherapy, or immunotherapy for locally advanced stages of cervical cancer, as well as for the systemic therapy for recurrent or metastatic cervical cancer. Aim: To summarize the current evidence for the diagnosis, treatment, and follow-up of cervical cancer and provide evidence-based clinical practice recommendations. Methods: Developed according to AGREE II standards, the guidelines classify scientific evidence based on the Agency for Health Technology Assessment and Tariff System criteria. Recommendations are graded by evidence strength and consensus level from the development group. Key Results: (1) Early-Stage Cancer: Stromal invasion and lymphovascular space involvement (LVSI) from pretreatment biopsy identify candidates for surgery, particularly for simple hysterectomy. (2) Surgical Approach: Minimally invasive surgery is not recommended, except for T1A, LVSI-negative tumors, due to a reduction in life expectancy. (3) Locally Advanced Cancer: concurrent chemoradiation (CCRT) followed by brachytherapy (BRT) is the cornerstone treatment. Low-risk patients (fewer than two metastatic nodes or FIGO IB2-II) may consider induction chemotherapy (ICT) followed by CCRT and BRT after 7 days. High-risk patients (two or more metastatic nodes or FIGO IIIA, IIIB, and IVA) benefit from pembrolizumab with CCRT and maintenance therapy. (4) Metastatic, Persistent, and Recurrent Cancer: A PD-L1 status from pretreatment biopsy identifies candidates for Pembrolizumab with available systemic treatment, while triplet therapy (Atezolizumab/Bevacizumab/chemotherapy) becomes a PD-L1-independent option. Conclusions: These evidence-based guidelines aim to improve clinical outcomes through precise treatment strategies based on individual risk factors, predictors, and disease stages. [ABSTRACT FROM AUTHOR]

In order to achieve the target of eliminating viral hepatitis as a public health threat by 2030 and to prioritize the role of hepatitis B vaccination in reducing new hepatitis B virus infections, the Chinese Preventive Medicine Association commissioned experts to develop the Expert Recommendations on Hepatitis B Vaccination in Adults to scientifically guide adult hepatitis B vaccination, build the herd immunity in population, and reduce the hepatitis B virus infection rate and incidence of hepatitis B. [ABSTRACT FROM AUTHOR]

La Sociedad Española de Reumatología (SER) reúne a la mayoría de los especialistas en Reumatología de España y entre los muchos servicios que ofrece a sus socios, cuenta con una Unidad de Investigación (UI). Esta unidad da apoyo metodológico en investigación clínica y epidemiológica a los socios de la SER, coordina y ejecuta proyectos de investigación, diseña y mantiene grandes bases de datos de pacientes, desarrolla proyectos de investigación cualitativa y realiza documentos de medicina basada en la evidencia (MBE). Mediante esta última actividad, la UI de la SER realiza guías de práctica clínica (GPC) y documentos de recomendaciones sobre temas relevantes para la reumatología, que cumplen con los estándares metodológicos más exigentes. El objetivo de este artículo es describir el proceso de gestión y la metodología seguidos por la UI de la SER para identificar los temas de sus documentos de MBE y como ejecuta y desarrolla sus Guías y Recomendaciones. The Spanish Society of Rheumatology (SER) brings together the majority of rheumatology specialists in Spain. One of the many services it offers its members is a Research Unit (RU-SER) that provides methodological support to SER members in clinical and epidemiological research, coordinates and carries out research projects, designs and maintains large patient databases, develops qualitative research projects and produces evidence-based medicine (EBM) documents. Through the latter activity, the RU-SER produces clinical practice guidelines and recommendations on topics relevant to rheumatology that meet the highest methodological standards. The aim of this article is to describe the management process and methodology used by the RU-SER to identify topics for EBM documents and how they are developed. [ABSTRACT FROM AUTHOR]

In the swiftly evolving domain of online commerce, the significance of recommendation systems has risen alongside the rapid expansion of internet usage over the past decade. As online commerce continues to thrive, recommendation systems serve a crucial function in steering users towards pertinent products amidst the expansive online environment. Nonetheless, conventional collaborative filtering algorithms often encounter challenges such as sparse data and shifting user preferences, necessitating innovative approaches. Our proposed recommendation system aims to tackle these hurdles by seamlessly blending collaborative filtering and content-based filtering methodologies. It will offer product suggestions for both new and existing users. Through thorough examination of users' past purchasing behaviors, patterns, and feedback, our system customizes recommendations precisely to cater to existing users' needs. The initial stage involves feature extraction, wherein both content-based and collaborative features are obtained by creating user profiles, computing content similarity, identifying related items, generating recommendations, and suggesting items. Additionally, new users will receive recommendations for novel and trending products. Leveraging the Walmart Product rating Dataset, our system continuously enhances recommendations based on evolving user interactions, thus optimizing engagement and satisfaction levels. This study underscores the pivotal role of advanced recommendation techniques in transforming the online commerce landscape, ensuring informed purchasing decisions, heightened user satisfaction, and increased sales. [ABSTRACT FROM AUTHOR]

Rust is a system‐level programming language with advantages in memory safety. It ensures that any Rust programs without unsafe code should not incur undefined behaviours. However, unsafe code still plays an essential role in Rust to achieve low‐level control. Therefore, a major design pattern of Rust programs is interior unsafe, which wraps unsafe code as safe APIs and handles all undefined behaviours internally. Rust standard library already provides a rich set of safe APIs to facilitate Rust code development. Nevertheless, due to unfamiliarity with these APIs, developers may misuse unnecessary unsafe code and suffer memory‐safety risks. In this paper, we investigate an approach to mitigate replaceable unsafe code. We first analyse unsafe APIs of the Rust standard library and summarize their common usage patterns. Each pattern corresponds to one or several code samples in our knowledge base. Then, we develop an approach to automatically recognize the usage pattern and recommend corresponding code samples. Our approach leverages dataflow analysis to exclude impossible patterns and employs a BERT‐based machine learning model to find the most similar pattern among the rest. We have conducted evaluation experiments with 472 unsafe code snippets collected from GitHub projects and successfully recognized the pattern of 394 snippets. We hope our approach can assist developers in detecting unnecessary unsafe code and suggesting safe alternatives. [ABSTRACT FROM AUTHOR]

We are currently in a period of upheaval, as many new technologies are emerging that open up new possibilities to shape our everyday lives. Particularly, within the field of Personalized Human-Computer Interaction we observe high potential, but also challenges. In this article, we explore how an increasing amount of online services and tools not only further facilitates our lives, but also shapes our lives and how we perceive our environments. For this purpose, we adopt the metaphor of personalized 'online layers' and show how these layers are and will be interwoven with the lives that we live in the 'human layer' of the real world. [ABSTRACT FROM AUTHOR]

Abstract With the rapid popularity of online social media, recommendation systems have increasingly harnessed social relations to enhance user-item interactions and mitigate the data sparsity issue. Beyond social connections, the semantic relatedness among items has emerged as a crucial factor in comprehending their inherent connections. In this work, we propose a novel Multi-view Contrastive learning framework for Social Recommendation, named MultiCSR. This framework adaptively incorporates user social networks and item knowledge graphs into modeling users preferences within recommendation systems. To facilitate the alignment of different views, we introduce a dedicated multi-view contrastive learning process that extracts rich information from each view and foster mutual enhancement. Extensive experiments conducted on three real-world datasets demonstrate the effectiveness of our framework over representative recommendation methods. Furthermore, ablation studies offer a deeper understanding of the mechanisms underlying our framework.

Objective: Technological advances have led to drastic changes in daily life, and particularly healthcare, while traditional diagnosis methods are being replaced by technology-oriented models and paper-based patient healthcare records with digital files. Using the latest technology and data mining techniques, we aimed to develop an automated clinical decision support system (CDSS), to improve patient prognoses and healthcare delivery. Our proposed approach placed a strong emphasis on improvements that meet patient, parent, and physician expectations. We developed a flexible framework to identify hepatitis, dermatological conditions, hepatic disease, and autism in adults and provide results to patients as recommendations. The novelty of this CDSS lies in its integration of rough set theory (RST) and machine learning (ML) techniques to improve clinical decision-making accuracy and effectiveness. Methods: Data were collected through various web-based resources. Standard preprocessing techniques were applied to encode categorical features, conduct min-max scaling, and remove null and duplicate entries. The most prevalent feature in the class and standard deviation were used to fill missing categorical and continuous feature values, respectively. A rough set approach was applied as feature selection, to remove highly redundant and irrelevant elements. Then, various ML techniques, including K nearest neighbors (KNN), linear support vector machine (LSVM), radial basis function support vector machine (RBF SVM), decision tree (DT), random forest (RF), and Naive Bayes (NB), were employed to analyze four publicly available benchmark medical datasets of different types from the UCI repository and Kaggle. The model was implemented in Python, and various validity metrics, including precision, recall, F1-score, and root mean square error (RMSE), applied to measure its performance. Results: Features were selected using an RST approach and examined by RF analysis and important features of hepatitis, dermatology conditions, hepatic disease, and autism determined by RST and RF exhibited 92.85 %, 90.90 %, 100 %, and 80 % similarity, respectively. Selected features were stored as electronic health records and various ML classifiers, such as KNN, LSVM, RBF SVM, DT, RF, and NB, applied to classify patients with hepatitis, dermatology conditions, hepatic disease, and autism. In the last phase, the performance of proposed classifiers was compared with that of existing state-of-the-art methods, using various validity measures. RF was found to be the best approach for adult screening of: hepatitis with accuracy 88.66 %, precision 74.46 %, recall 75.17 %, F1-score 74.81 %, and RMSE value 0.244; dermatology conditions with accuracy 97.29 %, precision 96.96 %, recall 96.96 %, F1-score 96.96 %, and RMSE value, 0.173; hepatic disease, with accuracy 91.58 %, precision 81.76 %, recall 81.82 %, F1-Score 81.79 %, and RMSE value 0.193; and autism, with accuracy 100 %, precision 100 %, recall 100 %, F1-score 100 %, and RMSE value 0.064. Conclusion: The overall performance of our proposed framework may suggest that it could assist medical experts in more accurately identifying and diagnosing patients with hepatitis, dermatology conditions, hepatic disease, and autism.

Abstract Atopic dermatitis often begins in infancy and follows a chronic course of exacerbations and remissions. The etiology is complex and involves numerous factors that contribute to skin barrier defect and inflammation. In the Middle East, the burden of atopic dermatitis is understudied. Epidemiological data specific to the Gulf region are scarce but reveal a prevalence of up to about 40% in the United Arab Emirates. Region-specific factors, such as the climate and the frequency of consanguineous marriages, may affect atopic dermatitis incidence, prevalence, and evolution over time. A panel of experts predominantly from the United Arab Emirates analyzed the evidence from published guidelines, and considered expert guidance and local treatment practices to develop clear recommendations for the management of atopic dermatitis in the United Arab Emirates. They encourage a systematic approach for the diagnosis and treatment, using disease severity scores and quality-of-life measurement tools. Treatment recommendations take into consideration both established therapies and the approved systemic biologics dupilumab and tralokinumab, and the Janus kinase inhibitors baricitinib, upadacitinib, and abrocitinib.

Despite the global implementation of COVID-19 mitigation measures, the disease continues to maintain transmission. Although mask wearing became one of the key measures for preventing the transmission of COVID-19 early in the pandemic period, many countries have relaxed the mandatory or recommended wearing of masks. The objective of the present study was to estimate the epidemiological impact of removing the mask-wearing recommendation in Japan. We developed a model to assess the consequences of declining mask-wearing coverage after the government revoked its recommendation in February 2023. The declining mask-wearing coverage was estimated using serial cross-sectional data, and a mathematical model was devised to determine the age-specific incidence of COVID-19 using the observed case count in Tokyo from week of October 3, 2022 to October 30, 2023. We explored model-based counterfactual scenarios to measure hypothetical situations in which the mask-wearing coverage decreases or increases relative to the observed coverage. The results show that mask-wearing coverage declined from 97% to 69% by the week of October 30, 2023, and that if the mask-wearing recommendation had continued, 427 lives could have been saved in Tokyo. If the mask-wearing coverage had declined to 25% of the observed level, the model suggests there might have been 1587 additional deaths. Thus, revoking the mask-wearing recommendation had a substantial epidemiological impact. In future pandemics, our proposed approach could provide a realtime quantification of the effects of relaxing countermeasures. [ABSTRACT FROM AUTHOR]

Abstract Learning the emotional tendency of travelers improves their interests and provide optimal traveling recommendations. This, however, requires large volumes of data such as travel plans, visit sites, personal interests, value for money, etc. for a detailed analysis. For ease of such analysis, in this article, an organized combinational control method (OC2M) is proposed. This method relies on conventional long short-term memory (LSTM) and fuzzy control (FC) to support such analysis. The first is responsible for filtering non-repeated data from the previous travel/ tour history reducing the data discreteness. This is organized non-recurrently to prevent outdated/ trivial data from influencing the consumer’s emotional learning. The FC process filters the adaptable data with the future tour/ travel plan for providing optimal recommendations that are liable to the consumer’s emotional tendency. This is identified based on the user's interest, preferences, and emotional connectivity with the place or plan from previous histories. Therefore, the proposed method improves the recommendation and validation tendencies of the consumer’s travel plan accordingly.

Continuous improvement of professional training was included in the monitoring of the progress of competencies by teachers, considering the importance of fulfilling students’ graduation profile at the end of their professional training. This study aims to evaluate students’ specific competencies in the Hospitality and Gastronomy programs at a private university located on the northern coast of Peru. The methodology is quantitative, applied to a population of 408 students during semesters 2020-1, 2020-2, and 2021-1; through the application of documentary review guides, the students’ competencies were evaluated, as well as the verification of the students’ satisfaction and recommendation regarding the teachers who evaluated such competencies. The results show that Hospitality students achieved 91% compliance with the graduation profile of the specific competencies and, in the case of Gastronomy – 81.25%. On the other hand, in 2020-1, Hospitality had a higher degree of satisfaction based on the teaching of the teachers who evaluated the competencies but a lower degree of recommendation, while Gastronomy evidenced a lower degree of satisfaction and a higher degree of recommendation. Likewise, in 2020-2, Gastronomy evidenced a higher degree of satisfaction and recommendation than Hospitality, and on the contrary, in 2021-1, Hospitality evidenced a higher degree of satisfaction and recommendation than that identified in Gastronomy. It is concluded that Hospitality students stand out with respect to the fulfillment of the graduation profile in semesters 2020-1 and 2021-1, and although there was a closeness in both programs in semester 2020-2, Hospitality always showed better results than Gastronomy.

Previous research on subscriptions has not focused on the characteristics of content where multiple content is consumed over a certain period, and evaluations of that are not easily associated. Therefore, considering these characteristics, this study theoretically and empirically examines the effects and interactions of conditions such as the ratio of good content, the content selection criterion, and the evaluation level of original content on consumer satisfaction and intention to extend. The results show that 1) the ratio of good content, i.e., the proportion of highly rated content, is the most influential factor for satisfaction and extension intention; 2) when the main criterion of content selection is the consumers themselves rather than recommendations by the streaming service, the difference in satisfaction based on the ratio of good content is significant; 3) the influence of original content exclusively planned and produced by the streaming service is relatively lower on satisfaction compared to the influence of the ratio of highly rated content, but similar on extension intention; and 4) the influence of original content on satisfaction is as high as that of the ratio of highly rated content, only when the content selection is done based on consumer preferences.

Chronic obstructive pulmonary disease (COPD) is a common disease that seriously harms human health, and it is also a leading cause of death, with a large number of cases being underdiagnosed. Can COPD screening enable early diagnosis of the disease and improve patient outcomes? In 2022, the U.S. Preventive Services Task Force (USPSTF) updated its "Chronic Obstructive Pulmonary Disease Screening Recommendation Statement" (hereinafter referred to as the "Recommendation") for asymptomatic adults by evaluating the latest research evidence regarding the benefits and harms of COPD screening. Based on limited evidence, the USPSTF issused a 'D' statement (there is moderate certainty that the screening has no net benefit) and recommends against COPD screening for asymptomatic adults, which is consistent with the 2016 version. The current article interprets the principles of this Recommendation in the context of Chinese general practice.

Graph convolutional neural networks have been successfully applied to collaborative filtering to capture high-quality user-item representations. Despite their remarkable performance, there are still limitations that hinder further improvement of recommender systems. Most existing recommendation systems utilize implicit feedback data for model training, but such data inevitably contains adversarial interaction noise. The conventional graph-based collaborative filtering method fails to effectively filter out this noise, and instead amplifies its impact, resulting in degraded model performance. To address this issue, we propose a robustness-enhanced collaborative filtering graph neural network model that does not rely on explicit noise filtering. Our approach involves simulating user-item interactions that do not exist in practice as adversarial interaction noise using random noise. To mitigate the impact of this noise in hidden feedback, we replace them with randomly selected partial nodes based on the principle of mutual information maximization. Our model has been extensively experimented on three public datasets (MovieLens-1 M, Yelp, and Ta-feng) and achieves performance improvements of about 5%, 10%, and 14%, respectively, compared to the state-of-the-art baseline model. In particular, in model robustness experiments, our model achieves significant performance improvements of about 13% and 17% in Yelp and Ta-feng. A comprehensive experimental study shows that our proposed method is reasonably effective and interpretable. [ABSTRACT FROM AUTHOR]

COVID-19 pandemic, the foodservice industry has had to modify the way it offers its services. The aim of this paper is to examine the drivers of intention to use and recommendation of online food delivery (OFD) using the SOR model, to analyze the perceived risk of COVID-19 and its relationship with the perceived risk for online purchase of OFD as well as to analyze the cultural effect between Spain and India. For this purpose, an online questionnaire was developed by obtaining a sample of 422 users and structural equation modeling (PLS-SEM) was used to determine which variables had a significant influence on the adoption of the OFD. The results confirm that attitude is the main antecedent of intention to use and recommendation, in contrast to the subjective norm relationships, where it was only confirmed by recommendation. This finding demonstrates how individuals' attitude toward intention and recommendation is more favorable than influence of third parties on decisions. [ABSTRACT FROM AUTHOR]

Most existing collaborative filtering-based recommender systems rely solely on available user–item interactions for user and item representation learning. Their performance often suffers significantly when interactions are sparse, as limited user and item interactions are insufficient for learning robust representations. To address this issue, recent research has explored additional information between users and items by leveraging the user–item bipartite graph. However, these methods have not fully exploited high-order neighborhood information, primarily using sampled interactions to enrich training data rather than integrating this information directly into representation learning. In this paper, we propose a novel model, EIR-GCN (Embedding Integration with Relational Graph Convolutional Network), which directly incorporates various types of collaborative relations, such as user–user and item–item interactions, into the embedding function for user preference modeling. Specifically, our model employs advanced graph convolutional network (GCN) techniques to integrate user–item, user–user, and item–item relations for comprehensive representation learning. EIR-GCN initially selects the most influential second-order neighbors from the user–item bipartite graph to form user–user and item–item connections. With these enriched connections, a message-passing method is adopted to learn node representations by aggregating messages from directly linked nodes, including first-order item neighbors and selected second-order user neighbors. Extensive experiments on several public datasets demonstrate that EIR-GCN outperforms strong baselines, including recent GCN-based models and those exploiting high-order information. Our results show that EIR-GCN achieves state-of-the-art performance and effectively addresses the sparsity issue, highlighting its robustness and efficacy in recommendation tasks. [ABSTRACT FROM AUTHOR]

Zinc, a vital trace element, holds significant importance in numerous physiological processes within the body. It participates in over 300 enzymatic reactions, metabolic functions, regulation of gene expression, apoptosis and immune modulation, thereby demonstrating its essential role in maintaining overall health and well-being. While zinc deficiency is associated with significant health risks, an excess of this trace element can also lead to harmful effects. According to the World Health Organization (WHO), 6.7 to 15 mg per day are referred to be the dietary reference value. An excess of the recommended daily intake may result in symptoms such as anemia, neutropenia and zincinduced copper deficiency. The European Food Safety Authority (EFSA) defines the tolerable upper intake level (UL) as 25 mg per day, whereas the Food and Drug Administration (FDA) allows 40 mg per day. This review will summarize the current knowledge regarding the calculation of UL and other health risks associated with zinc. For example, zinc intake is not limited to oral consumption; other routes, such as inhalation or topical application, may also pose risks of zinc intoxication. [ABSTRACT FROM AUTHOR]

With the rapid development of cloud computing paradigm and mobile devices, the number of cloud services and user interest data are growing explosively. It becomes harder for users to find suitable cloud services within satisfying response time. Thus a suitable cloud service automatic recommendation system is needed to solve the above problem. In this work, we propose KG2Rec, a novel recommendation method based on Knowledge Graph to provide users with appropriate recommendations to meet their real-time needs. The property-specific knowledge graph model is designed to model user–item and item–item bipartite relations. Based on the property-specific knowledge graph model, property-specific user–item relation features can be mined from it. Then the user–item relation features will be integrated into an improved collaborative filtering algorithm based on the local sensitive hash technique for Top-N item recommendation. Finally, we evaluate the proposed method in terms of Top-N recommendation on the MovieLens dataset, and prove it outperforms numbers of state-of-the-art recommendation systems. In addition, we prove it has well performance in term of long tail recommendation and big data processing. [ABSTRACT FROM AUTHOR]

Policy evaluation is now recognised by many governments as fundamental to ensuring that policies are informed by quality evidence. Nevertheless, the practice of policy evaluation is still difficult to systematise inside governments. Building on more than 20 years of work on this topic, the Organisation for Economic Co-operation and Development Recommendation on Public Policy Evaluation, adopted in 2022, represents the first international legal guidance on the topic. It identifies three essential pillars to ensure the impact of policy evaluation: institutionalisation, quality and use. This article explains how the Organisation for Economic Co-operation and Development Recommendation was developed, what these three pillars cover, and reflect on how the Organisation for Economic Co-operation and Development can further help countries developing a sound evaluation system. [ABSTRACT FROM AUTHOR]

The article introduces FairGap, a novel approach for fairness-aware recommendation systems, which directly intervenes in observed interactions to generate a counterfactual graph, enabling fair representations for users and items. Topics include the challenges of biased user-item interactions, the FairGap framework for generating counterfactual graphs, and its effectiveness in improving recommendation fairness through theoretical proofs and experimental validation.

Clinical Practice Guideline on Acupuncture and Moxibustion: Migraine (WFAS 007.9-2023) is a clinical practice guideline officially released by the World Federation of Acupuncture-Moxibustion Societies (WFAS) on October 9, 2023, and is the first international guideline on the treatment of migraine with acupuncture. This international standard was developed under the guidance of rigorous evidence-based methodology, and it contains guideline purpose, scope, applicable population, applicable settings, overview of acupuncture for migraine, guideline development process and recommendations. For promoting the understanding and application of this guideline, this article summarizes a total of 18 recommendations in order to assist clinical decisions for migraine with acupuncture. [ABSTRACT FROM AUTHOR]

Closed-loop imaging programs (CLIPs) are designed to ensure that patients receive appropriate follow-up, but a review of incidental CT-detected breast findings in the setting of CLIPs has not been performed. A retrospective review was conducted of CT reports at a single academic institution from July 1, 2020, to January 31, 2022, to identify reports with recommendations for breast imaging follow-up. Medical records were reviewed to evaluate patient adherence to follow-up, CLIP intervention, subsequent BI-RADS assessment, and diagnosis. Adherence was defined as diagnostic breast imaging performed within 6 months of the CT recommendation. Follow-up recommendations for breast imaging were included in CT report impressions for 311 patients. Almost half of patients (47.3% [147 of 311]) underwent follow-up breast imaging within 6 months, yielding breast cancer diagnoses in 12.9% (19 of 147) and a biopsy-proven positive predictive value of 65.5% (19 of 29). Most patients who returned for follow-up within 6 months did so without CLIP intervention. The majority of CT report impressions in the follow-up group (85.0% [125 of 147]) contained specific recommendations for "diagnostic breast imaging." For patients who did not receive follow-up, the CLIP team tracked all cases and intervened in 19.1% (28 of 147). The most common intervention was a phone call and/or fax to the primary care provider. Outpatient CT examination setting and specific recommendation for diagnostic breast imaging were significantly associated with higher follow-up adherence (P <.0001). Actionable CT-detected breast findings require follow-up diagnostic breast imaging because of a relevant cancer detection rate of 12.9%. Although many patients return for breast imaging without intervention, almost half of patients did not receive follow-up and may account for a significant number of missed cancer diagnoses. Specific CT recommendation verbiage is associated with higher follow-up adherence, which can be addressed across settings even without CLIPs. [Display omitted] [ABSTRACT FROM AUTHOR]

Multi-scenario and multi-task recommendation can use various feedback behaviors of users in different scenarios to learn users' preferences and then make recommendations, which has attracted attention. However, the existing work ignores feature interactions and the fact that a pair of feature interactions will have differing levels of importance under different scenario-task pairs, leading to sub-optimal user preference learning. In this article, we propose a Multi-scenario and Multi-task aware Feature Interaction model, dubbed MMFI, to explicitly model feature interactions and learn the importance of feature interaction pairs in different scenarios and tasks. Specifically, MMFI first incorporates a pairwise feature interaction unit and a scenario-task interaction unit to effectively capture the interaction of feature pairs and scenario-task pairs. Then MMFI designs a scenario-task aware attention layer for learning the importance of feature interactions from coarse-grained to fine-grained, improving the model's performance on various scenario-task pairs. More specifically, this attention layer consists of three modules: a fully shared bottom module, a partially shared middle module, and a specific output module. Finally, MMFI adapts two sparsity-aware functions to remove some useless feature interactions. Extensive experiments on two public datasets demonstrate the superiority of the proposed method over the existing multi-task recommendation, multi-scenario recommendation, and multi-scenario & multi-task recommendation models. [ABSTRACT FROM AUTHOR]

Background: There is practice heterogeneity in the use, type, and duration of prophylactic antiseizure medications (ASMs) in patients with moderate–severe traumatic brain injury (TBI). Methods: We conducted a systematic review and meta-analysis of articles assessing ASM prophylaxis in adults with moderate–severe TBI (acute radiographic findings and requiring hospitalization). The population, intervention, comparator, and outcome (PICO) questions were as follows: (1) Should ASM versus no ASM be used in patients with moderate–severe TBI and no history of clinical or electrographic seizures? (2) If an ASM is used, should levetiracetam (LEV) or phenytoin/fosphenytoin (PHT/fPHT) be preferentially used? (3) If an ASM is used, should a long versus short (> 7 vs. ≤ 7 days) duration of prophylaxis be used? The main outcomes were early seizure, late seizure, adverse events, mortality, and functional outcomes. We used Grading of Recommendations Assessment, Development, and Evaluation (GRADE) methodology to generate recommendations. Results: The initial literature search yielded 1998 articles, of which 33 formed the basis of the recommendations: PICO 1: We did not detect any significant positive or negative effect of ASM compared to no ASM on the outcomes of early seizure, late seizure, adverse events, or mortality. PICO 2: We did not detect any significant positive or negative effect of PHT/fPHT compared to LEV for early seizures or mortality, though point estimates suggest fewer late seizures and fewer adverse events with LEV. PICO 3: There were no significant differences in early or late seizures with longer versus shorter ASM use, though cognitive outcomes and adverse events appear worse with protracted use. Conclusions: Based on GRADE criteria, we suggest that ASM or no ASM may be used in patients hospitalized with moderate–severe TBI (weak recommendation, low quality of evidence). If used, we suggest LEV over PHT/fPHT (weak recommendation, very low quality of evidence) for a short duration (≤ 7 days, weak recommendation, low quality of evidence). [ABSTRACT FROM AUTHOR]

Rapid review (RR) could accelerate the traditional systematic review (SR) process by simplifying or omitting steps using various shortcuts. With the increasing popularity of RR, numerous shortcuts had emerged, but there was no consensus on how to choose the most appropriate ones. This study conducted a literature search in PubMed from inception to December 21, 2023, using terms such as "rapid review" "rapid assessment" "rapid systematic review" and "rapid evaluation". We also scanned the reference lists and performed citation tracking of included impact studies to obtain more included studies. We conducted a narrative synthesis of all RR approaches, shortcuts and studies assessing their effectiveness at each stage of RRs. Based on the current evidence, we provided recommendations on utilizing certain shortcuts in RRs. Ultimately, we identified 185 studies focusing on summarizing RR approaches and shortcuts, or evaluating their impact. There was relatively sufficient evidence to support the use of the following shortcuts in RRs: limiting studies to those published in English‐language; conducting abbreviated database searches (e.g., only searching PubMed/MEDLINE, Embase, and CENTRAL); omitting retrieval of grey literature; restricting the search timeframe to the recent 20 years for medical intervention and the recent 15 years for reviewing diagnostic test accuracy; conducting a single screening by an experienced screener. To some extent, the above shortcuts were also applicable to SRs. This study provided a reference for future RR researchers in selecting shortcuts, and it also presented a potential research topic for methodologists. [ABSTRACT FROM AUTHOR]

Recent years have seen rapid progress in network representation learning, which removes the need for burdensome feature engineering and facilitates downstream network-based tasks. In reality, networks often exhibit heterogeneity, which means there may exist multiple types of nodes and interactions. Heterogeneous networks raise new challenges to representation learning, as the awareness of node and edge types is required. In this article, we study a basic building block of general heterogeneous networks, the heterogeneous networks with two types of nodes. Many problems can be solved by decomposing general heterogeneous networks into multiple bipartite ones. Recently, to overcome the demerits of non-metric measures used in the embedding space, metric learning-based approaches have been leveraged to tackle heterogeneous network representation learning. These approaches first generate triplets of samples, in which an anchor node, a positive counterpart, and a negative one co-exist, and then try to pull closer positive samples and push away negative ones. However, when dealing with heterogeneous networks, even the simplest two-typed ones, triplets cannot simultaneously involve both positive and negative samples from different parts of networks. To address this incompatibility of triplet-based metric learning, in this article, we propose a novel quintuple-based method for learning node representations in bipartite heterogeneous networks. Specifically, we generate quintuples that contain positive and negative samples from two different parts of networks. And we formulate two learning objectives that accommodate quintuple-based learning samples, a proximity-based loss that models the relations in quintuples by sigmoid probabilities and an angular loss that more robustly maintains similarity structures. In addition, we also parameterize feature learning by using one-dimensional convolution operators around nodes' neighborhoods. Compared with eight methods, extensive experiments on two downstream tasks manifest the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

There is no established standard of care for treating central centrifugal cicatricial alopecia (CCCA), and treatment approaches vary widely. To develop consensus statements regarding the use of various pharmacological therapies in treating adults with CCCA. We invited 27 dermatologists with expertise in hair and scalp disorders to participate in a 3-round modified Delphi study between January and March 2023. Statements met strong consensus if 75% of respondents agreed or disagreed. Statements met moderate consensus if 55% or more but less than 75% agreed or disagreed. In round 1, 5 of 33 (15.2%) statements met strong consensus, followed by 9 of 28 (32.1%) in round 2. After the final round 3 meeting, strong consensus was reached for 20 of 70 (28.6%) overall statements. Two statements achieved moderate consensus. This study included only English-speaking, US-based dermatologists and did not consider nonpharmacological therapies. Despite varying opinions among dermatologists, consensus was reached for several statements to help clinicians manage CCCA. We also highlight areas that lack expert consensus with the goal of advancing research and therapeutic options for CCCA. [ABSTRACT FROM AUTHOR]

For psoriatic patients who need to receive nonlive or live vaccines, evidence-based recommendations are needed regarding whether to pause or continue systemic therapies for psoriasis and/or psoriatic arthritis. To evaluate literature regarding vaccine efficacy and safety and to generate consensus-based recommendations for adults receiving systemic therapies for psoriasis and/or psoriatic arthritis receiving nonlive or live vaccines. Using a modified Delphi process, 22 consensus statements were developed by the National Psoriasis Foundation Medical Board and COVID-19 Task Force, and infectious disease experts. Key recommendations include continuing most oral and biologic therapies without modification for patients receiving nonlive vaccines; consider interruption of methotrexate for nonlive vaccines. For patients receiving live vaccines, discontinue most oral and biologic medications before and after administration of live vaccine. Specific recommendations include discontinuing most biologic therapies, except for abatacept, for 2-3 half-lives before live vaccine administration and deferring next dose 2-4 weeks after live vaccination. Studies regarding infection rates after vaccination are lacking. Interruption of antipsoriatic oral and biologic therapies is generally not necessary for patients receiving nonlive vaccines. Temporary interruption of oral and biologic therapies before and after administration of live vaccines is recommended in most cases. [ABSTRACT FROM AUTHOR]

We investigate whether the text of analyst reports can provide additional information beyond the recommendation and target price. Positive and negative word lexicons are generated through an automated Bayesian learning method applied to Korean analyst reports spanning from 2016 to 2018. Then, the textual tone of an analyst report is quantified as the difference between the frequencies of positive and negative words in the text. The announcement returns of portfolios sorted by textual tone exhibit significant differences ranging from 1.14% to 2.82% within the same recommendation or target price revision group. Regression analysis also reveals significant association between the textual tone of analyst reports and stock announcement returns, even when controlling for the recommendation and target price. Notably, the text proves to be more informative in negative tones and within firms with limited analyst coverage. Our results indicate that textual analysis can unveil nuanced analyst opinions not captured by numerical information. [ABSTRACT FROM AUTHOR]

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