Background The prevalence of allergic fungal rhinosinusitis (AFRS) and its associated risk factors have been an issue of debate. Some epidemiological factors have been correlated to the disease prevalence. Objectives To observe the prevalence pattern of AFRS worldwide and to investigate the effect of specific epidemiological factors on the disease prevalence. Methods A systematic review was derived from 1983 to December 2018. Data on the prevalence of AFRS were collected from the selected studies. Relevant factors assessing each city's climate, socioeconomics and geography were used to study the association with AFRS prevalence. Results 35 cities across 5 continents were investigated. The worldwide average rate of AFRS in CRS cases is 7.8% (0.2%–26.7%) in which more than half of the investigated cities (57%) had low AFRS prevalence, while the remaining cities had intermediate (11%) and high (32%) prevalence. Cities with higher temperatures were associated with a higher prevalence of AFRS ( p-value 0.002), whereas cities with humid continental climate were significantly associated with a low prevalence of AFRS ( p-value 0.032). Humidity and wind speed were lower in the cities with higher AFRS prevalence ( p-value 0.018 and 0.008, respectively). There were no significant correlations between AFRS prevalence and economic levels, presence of water bodies, rainfall amounts, altitude, and presence of forests. Conclusion AFRS has a worldwide distribution pattern with varying prevalence. In this ecological study, we observed a correlation between AFRS prevalence and climatic factors (climate classification, humidity, temperature, and wind speed). Socioeconomic factors should be analyzed on an individual basis for better assessment of the relationship with disease prevalence.
ObjectiveThe aim of this study was to assess the ability of drug-induced sleep endoscopy (DISE) to change therapeutic decisions through the identification of obstruction sites in patients with obstructive sleep apnea (OSA).Materials and methodsA systematic review and meta-analysis were conducted concerning studies that reported the impact of DISE on therapeutic recommendations. The percentage of change was collected for each study and per site of the collapse. The pooled rate of change and the respective 95% confidence interval (CI) were computed. Subgroup analysis was performed based on patients’ age, sample size, the applied DISE protocol, and the originally used diagnostic modality before DISE.ResultsIn a total of nine studies, 1247 patients were included (69.2% males, 59.7% children, 78.04% with a multilevel collapse). Therapeutic decisions changed in 43.69% of patients (CI, 33.84 to 53.54). The change rates were significantly higher in adults (54.0% versus 25.9% in children, P = 0.001), midazolam-based DISE protocols (78.4% versus 48.45% for midazolam plus propofol and 33.9% for propofol, P < 0.001), and after awake endoscopy (62.2% as compared to 44.6% after clinical basic examination [CBE], 40.1% after CBE, lateral cephalometry, and Müller maneuver, P = 0.02). Changes at uvular and palatal sites were more frequent in adults and at the tonsils in children.ConclusionThe DISE approach can be promoted via implementing unified classification systems of obstruction sites; the widescale application of target-controlled infusion and its therapeutic benefits can be explored in well-designed randomized studies that compare its efficacy with other diagnostic modalities.
The Internet has a huge amount of information when it comes to analysis, much of which is valuable and significant. Arabic Sentiment Analysis (SA) is a method responsible for analyzing people’s thoughts, feelings, and responses to a variety of products and services on social networking and commercial sites. Several researchers utilize sentiment analysis to determine the opinions of customers in various areas, including e-marketing, business, and other fields. Deep learning (DL) is a useful technology for developing sentiment analysis models to improve e-marketing operations. There are a few studies targeting Arabic sentiment analysis (ASA) in e-marketing using deep learning algorithms. Due to a number of difficulties in the Arabic language, such as the language’s morphological features, the diversity of dialects, and the absence of suitable corpora, sentiment analysis on Arabic material is restricted. In this paper, we will compare several Arabic sentiment analysis models. Also, we discuss the deep learning algorithms that are employed in Arabic sentiment analysis. The domain of the collected papers is Arabic sentiment analysis in e-marketing using deep learning. Our first contribution is to introduce and present deep learning models that are used in ASA. Secondly, investigate and study Arabic datasets utilized for Arabic sentence analysis. We create and develop a new Arabic dataset for Saudi Arabian communication companies, namely Sara-Dataset, to increase the quality and quantity of their services. Third, each collected study is assessed in terms of its methodology, contributions, deep learning techniques, performance, Arabic datasets in emarketing, and potential improvements in developing Arabic sentiment analysis models. Fourth, we analyzed several papers’ performance in terms of accuracy, F-measure, recall, pre-procession, and area under the curve (AUC). Also, our comparative analysis includes feature selection (e.g., domain-specific selection) methods that are used in Arabic sentiment analysis. Fifth, we also discuss how to improve Arabic sentiment analysis using preprocessing techniques (e.g., word embedding). Finally, we provide a design model for analyzing Arabic sentiment about communications services provided by Saudi Arabian enterprises.
Background: Burnout is a syndrome conceptualized as resulting from chronic workplace stress that has not been successfully managed. Burnout has been described in health-care workers and has been reported to be common in primary health care physicians. This study aimed is to assess the prevalence rate of burnout, and its associated factors, amongst primary care doctors in cluster one area, Riyadh, Saudi Arabia. Methods: This study is a cross-sectional in design. The subjects consist of general physicians, residents, registrars, senior registrars, consultants, nurses and receptionist. This study is conducted at specialized consulting clinics in Riyadh. The questionnaire's primary outcome measures are based on the Maslach scale. The Maslach Burnout Inventory (MBI) is an industry-standard that has been administered across large samples of varied professions in many countries. It has three dimensions: emotional exhaustion (EE), depersonalization (DP), and personal accomplishment (PA). Results:A high level of burnout was found among PHC staff in Riyadh, Saudi Arabia. The factors affecting Emotional exhaustion (EE) of the PHC staff are the position, gender, working hours per week, the degree of job satisfaction, changing the job, planning to leave the job, the salary, depersonalization and personal accomplishment. Whereas the factors affecting Depersonalization (DP) among PHC staff are: Work night shifts, the degree of job satisfaction, changing the job, planning to leave the job, Personal Accomplishment and Emotional Exhaustion. Similarly, changing the job, EE, and DP are the significant factors affecting the personal accomplishment (PA) of PHC staff. Conclusion: Burnout is an important issue related to primary care staff in Riyadh, Saudi Arabia. Proper assessment and determination of the prevalence rate of burnout, and its associated factors is critical.
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