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(1) Background: Lung cancer's high mortality due to late diagnosis highlights a need for early detection strategies. Artificial intelligence (AI) in healthcare, particularly for lung cancer, offers promise by analyzing medical data for early identification and personalized treatment. This systematic review evaluates AI's performance in early lung cancer detection, analyzing its techniques, strengths, limitations, and comparative edge over traditional methods. (2) Methods: This systematic review and meta-analysis followed the PRISMA guidelines rigorously, outlining a comprehensive protocol and employing tailored search strategies across diverse databases. Two reviewers independently screened studies based on predefined criteria, ensuring the selection of high-quality data relevant to AI's role in lung cancer detection. The extraction of key study details and performance metrics, followed by quality assessment, facilitated a robust analysis using R software (Version 4.3.0). The process, depicted via a PRISMA flow diagram, allowed for the meticulous evaluation and synthesis of the findings in this review. (3) Results: From 1024 records, 39 studies met the inclusion criteria, showcasing diverse AI model applications for lung cancer detection, emphasizing varying strengths among the studies. These findings underscore AI's potential for early lung cancer diagnosis but highlight the need for standardization amidst study variations. The results demonstrate promising pooled sensitivity and specificity of 0.87, signifying AI's accuracy in identifying true positives and negatives, despite the observed heterogeneity attributed to diverse study parameters. (4) Conclusions: AI demonstrates promise in early lung cancer detection, showing high accuracy levels in this systematic review. However, study variations underline the need for standardized protocols to fully leverage AI's potential in revolutionizing early diagnosis, ultimately benefiting patients and healthcare professionals. As the field progresses, validated AI models from large-scale perspective studies will greatly benefit clinical practice and patient care in the future.
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Atopic dermatitis is a complex, recurrent, chronic inflammatory skin condition. It frequently begins to manifest in early childhood and may last throughout adulthood. The need for clinical practice guidelines that are based on evidence is critical for efficient and secure care. Little is known about how primary care providers (PCPs) should handle pediatric and adult atopic dermatitis cases and whether they should follow national recommendations. Our systemic review aimed to examine management strategies for treating adult and pediatric (family) atopic dermatitis, including topical calcineurin inhibitors (TCIs), topical corticosteroids (TCS), skin emollients, oral antihistamines, and diet. Data sources were PubMed (MEDLINE) and Embase. Our review investigated English-language articles from 2014 to 2023 that studied the management of adult and children atopic dermatitis. Overall, there were 15 articles included. Surveys and analyses of national databases were the most widely used methods (n=7). The use of TCS by PCPs was common, but they also overprescribed nonsedating antihistamines, favored low-potency drugs, and avoided TCIs. Most studies relied on healthcare personnel reporting their typical behaviors rather than looking at specific patient encounters and it is considered a limitation. Finally, there are gaps in knowledge and management of critical topics such as prescribing TCIs and understanding the safety profiles of TCS, when it comes to treating adult and pediatric atopic dermatitis. Future research in this area is urgently needed because the current systemic assessment is mostly restricted to small studies that assess prescribing behaviors with scant information describing nonmedication management.
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Background Job satisfaction in the nursing field directly impacts the quality of patient care. However, increased work demand puts nurses at a higher risk of job dissatisfaction, which can, in turn, affect their work performance. This study aimed to measure job satisfaction among nurses working in National Guard Primary Healthcare Centers (PHCs) and to determine the different sources of pressure at their workplace. Methods A cross-sectional quantitative study was conducted among nurses working in the National Guard PHCs in the Makkah region, Saudi Arabia, in 2022. A validated questionnaire from previous literature was used to evaluate nurses' job satisfaction. Results A total of 77 nurses completed the questionnaire, with an overall response rate of 89.5%. While 58% (n=45) of nurses were satisfied, 42% (n=32) were dissatisfied. Approximately half the participants were dissatisfied with the rate of payment (49%, n=38), working hours (47%, n=36), and future chances of promotion (44%, n=34). Moreover, 51% (n=39) of nurses attributed considerable pressure to staff shortage and 44% (n=34) to workload. Furthermore, lower mean satisfaction scores in nurses were significantly associated with their intention to leave their current center (p-value= 0.06). In addition, reduced satisfaction scores were frequently observed among females, singles, those who finished their first nurse training five to 10 years ago, those who had a previous experience outside the Ministry of National Guard Health Affairs (MNGHA), those who had only one to five service years, and the ones who belonged to centers that did not have clearly stated standards and policies for nursing practice. However, these associations were statistically not significant. Conclusion Results indicate that nurses' job satisfaction should be improved to decrease nurses' intention to leave their workplace and maintain their optimum performance in patient care. This can be achieved by addressing the sources of dissatisfaction and pressure at work.
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Background As a well-documented fact, metastatic brain tumors are the most common cause of brain tumors in adults, with an incidence of 9-17%, based on various studies, although it was thought to be higher. The aim of this study was to describe recorded cases of metastatic brain tumors in the adult population of a tertiary care and oncology center in Jeddah, Saudi Arabia. Methods This study was conducted at King Abdulaziz Medical City (KAMC) at King Khalid Hospital in Jeddah, Saudi Arabia, including records from January 2016 to December 2020. The study implemented a retrospective cohort design to fulfill its aim. A data collection sheet containing demographic data such as age and gender, and information pertaining to the primary pathology, multiplicity, and survival outcome was used. Results A total number of 213 patients were enrolled in this study. Overall, 68.1% of the sample comprised of females. Approximately two-thirds (61.9%) of the patients' imaging results revealed multiplicity, whereas the remaining third (38.1%) had solitary lesions. The estimated overall survival median after the diagnosis of brain metastasis was six months (95% CI: 5.5-6.5). Conclusion We recommend conducting a nationwide study to better understand the incidence in accordance to geographical and gender differences. We can further expand our research to include other institutes in Saudi Arabia, and include important predictors such as time from the diagnosis of primary pathology to brain metastasis, disease progression cost, and disease progression in the months prior to the patients' death.