Modeling the impact of the oil sector on the economy of sultanate of Oman

This study constructs and analyses a simple macroeconomic model that specifically tailored to model the impact of oil sector on the economy of Sultanate of Oman. The constructed model of the study measures the impact of oil sector on the Oman economy for the last three decades and also provides some forecasting for the major macroeconomics indicators related to the Oman economy. Model simulations indicate that the oil sector has large and positive impact on Oman gross domestic product and its influence spills over to all other non-oil sectors of Oman economy. The study found that largest influence of oil was on the gas sector and the least economic sector influenced by oil was agricultural sector. The findings of the study suggest that Oman economy is far from being diversified and that the proposed model helps the policy makers in Oman to identify and forecast the impact of oil on other components of the Oman economy

Epidemiology of End Stage Renal Disease (ESRD) of Oman since 1983 to 2018: a secondary data analysis study

No abstract available

Care of Patients with Diabetic Foot Disease in Oman

Diabetes mellitus is a major public health challenge and causes substantial morbidity and mortality worldwide. Diabetic foot disease is one of the most debilitating and costly complications of diabetes. While simple preventative foot care measures can reduce the risk of lower limb ulcerations and subsequent amputations by up to 85%, they are not always implemented. In Oman, foot care for patients with diabetes is mainly provided in primary and secondary care settings. Among all lower limb amputations performed in public hospitals in Oman between 2002–2013, 47.3% were performed on patients with diabetes. The quality of foot care among patients with diabetes in Oman has not been evaluated and unidentified gaps in care may exist. This article highlights challenges in the provision of adequate foot care to Omani patients with diabetes. It concludes with suggested strategies for an integrated national diabetic foot care programme in Oman

Care of Patients with Diabetic Foot Disease in Oman

Diabetes mellitus is a major public health challenge and causes substantial morbidity and mortality worldwide. Diabetic foot disease is one of the most debilitating and costly complications of diabetes. While simple preventative foot care measures can reduce the risk of lower limb ulcerations and subsequent amputations by up to 85%, they are not always implemented. In Oman, foot care for patients with diabetes is mainly provided in primary and secondary care settings. Among all lower limb amputations performed in public hospitals in Oman between 2002–2013, 47.3% were performed on patients with diabetes. The quality of foot care among patients with diabetes in Oman has not been evaluated and unidentified gaps in care may exist. This article highlights challenges in the provision of adequate foot care to Omani patients with diabetes. It concludes with suggested strategies for an integrated national diabetic foot care programme in Oman

Decision-to-Delivery Time Intervals in Emergency Caesarean Section Cases : Repeated cross-sectional study from Oman

Objectives: In cases of fetal intolerance to labour, meeting the standard decision-to-delivery time interval (DDI) of ≤30 minutes is challenging. This study aimed to assess DDIs in emergency Caesarean section (CS) cases to identify factors causing DDI delays and the impact of a delayed DDI on perinatal outcomes. Methods: This repeated cross-sectional study included all emergency CS procedures performed due to acute fetal distress, antepartum haemorrhage or umbilical cord prolapse at the Nizwa Hospital, Nizwa, Oman. Three audit cycles of three months each were conducted between April 2011 and June 2013, including an initial retrospective cycle and two prospective cycles following the implementation of improvement strategies to address factors causing DDI delays. Poor perinatal outcomes were defined as Apgar scores of <7 at five minutes, admission to the Special Care Baby Unit (SCBU) or a stillbirth. Results: In the initial cycle, a DDI of ≤30 minutes was achieved in 23.8% of 84 cases in comparison to 44.6% of 83 cases in the second cycle. In the third cycle, 60.8% of 79 women had a DDI of ≤30 minutes (P <0.001). No significant differences in perinatal outcomes for cases with a DDI of ≤30 minutes versus 31–60 minutes were observed; however, a DDI of >60 minutes was significantly associated with poor neonatal outcomes in terms of increased SCBU admissions and low Apgar scores (P <0.001 each). Factors causing DDI delays included obtaining consent for the CS procedure, a lack of operating theatre availability and moving patients to the operating theatre. Conclusion: The identification of factors causing DDI delays may provide opportunities to improve perinatal outcomes

Diabetic Foot Disease Research in Gulf Cooperation Council Countries: A bibliometric analysis

Objectives: Countries in the Gulf Cooperation Council (GCC) have some of the highest prevalence rates of diabetes mellitus (DM) in the world; however, DM-related research activity in this region is limited. This study aimed to examine trends in published diabetic foot disease (DFD) research undertaken in GCC countries. Methods: This bibliometric study was conducted in December 2016. Standardised criteria were used to search the MEDLINE® database (National Library of Medicine, Bethesda, Maryland, USA) for DFD-related publications authored by GCC researchers between January 1990 and December 2015. Various details such as the type of publication, journal impact factor and number of article citations were analysed. Results: A total of 96 research articles were identified. The number of publications per year significantly increased from nil prior to 1991 to 15 in 2015 (P <0.01). Basic/clinical research articles accounted for 96.9% of publications, with three randomised controlled trials and no systematic reviews/meta-analyses. When adjusted for population size, Kuwait had the highest number of published papers per year, followed by Bahrain and Qatar. The number of authors per publication significantly increased during the study period (P = 0.02). However, 16 articles (16.7%) had no citations. The median journal impact factor was 0.15 ± 1.19 (range: 0–6.04). Conclusion: The number of publications authored by GCC researchers has risen in recent years. Increasing research funding and promoting collaboration between local and international researchers and institutes are recommended to bolster research regarding DFD prevention and management in GCC countries. Keywords: Bibliometric Analysis; Diabetes Mellitus; Diabetic Foot; Research; Publications; Arab Countries; Gulf Cooperation Council

Deep learning to automate the labelling of head MRI datasets for computer vision applications

OBJECTIVES: The purpose of this study was to build a deep learning model to derive labels from neuroradiology reports and assign these to the corresponding examinations, overcoming a bottleneck to computer vision model development. METHODS: Reference-standard labels were generated by a team of neuroradiologists for model training and evaluation. Three thousand examinations were labelled for the presence or absence of any abnormality by manually scrutinising the corresponding radiology reports ('reference-standard report labels'); a subset of these examinations (n = 250) were assigned 'reference-standard image labels' by interrogating the actual images. Separately, 2000 reports were labelled for the presence or absence of 7 specialised categories of abnormality (acute stroke, mass, atrophy, vascular abnormality, small vessel disease, white matter inflammation, encephalomalacia), with a subset of these examinations (n = 700) also assigned reference-standard image labels. A deep learning model was trained using labelled reports and validated in two ways: comparing predicted labels to (i) reference-standard report labels and (ii) reference-standard image labels. The area under the receiver operating characteristic curve (AUC-ROC) was used to quantify model performance. Accuracy, sensitivity, specificity, and F1 score were also calculated. RESULTS: Accurate classification (AUC-ROC > 0.95) was achieved for all categories when tested against reference-standard report labels. A drop in performance (ΔAUC-ROC > 0.02) was seen for three categories (atrophy, encephalomalacia, vascular) when tested against reference-standard image labels, highlighting discrepancies in the original reports. Once trained, the model assigned labels to 121,556 examinations in under 30 min. CONCLUSIONS: Our model accurately classifies head MRI examinations, enabling automated dataset labelling for downstream computer vision applications. KEY POINTS: • Deep learning is poised to revolutionise image recognition tasks in radiology; however, a barrier to clinical adoption is the difficulty of obtaining large labelled datasets for model training. • We demonstrate a deep learning model which can derive labels from neuroradiology reports and assign these to the corresponding examinations at scale, facilitating the development of downstream computer vision models. • We rigorously tested our model by comparing labels predicted on the basis of neuroradiology reports with two sets of reference-standard labels: (1) labels derived by manually scrutinising each radiology report and (2) labels derived by interrogating the actual images

“So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy

Transformative artificially intelligent tools, such as ChatGPT, designed to generate sophisticated text indistinguishable from that produced by a human, are applicable across a wide range of contexts. The technology presents opportunities as well as, often ethical and legal, challenges, and has the potential for both positive and negative impacts for organisations, society, and individuals. Offering multi-disciplinary insight into some of these, this article brings together 43 contributions from experts in fields such as computer science, marketing, information systems, education, policy, hospitality and tourism, management, publishing, and nursing. The contributors acknowledge ChatGPT's capabilities to enhance productivity and suggest that it is likely to offer significant gains in the banking, hospitality and tourism, and information technology industries, and enhance business activities, such as management and marketing. Nevertheless, they also consider its limitations, disruptions to practices, threats to privacy and security, and consequences of biases, misuse, and misinformation. However, opinion is split on whether ChatGPT's use should be restricted or legislated. Drawing on these contributions, the article identifies questions requiring further research across three thematic areas: knowledge, transparency, and ethics; digital transformation of organisations and societies; and teaching, learning, and scholarly research. The avenues for further research include: identifying skills, resources, and capabilities needed to handle generative AI; examining biases of generative AI attributable to training datasets and processes; exploring business and societal contexts best suited for generative AI implementation; determining optimal combinations of human and generative AI for various tasks; identifying ways to assess accuracy of text produced by generative AI; and uncovering the ethical and legal issues in using generative AI across different contexts