Background: Early diagnosis of human immunodeficiency virus (HIV) is important because antiretroviral therapies are more effective if infected individuals are diagnosed early. Diagnosis of HIV relies on laboratory testing and determining the demographic and clinical characteristics of undiagnosed HIV-infected patients may be useful in identifying patients for testing. This systematic review aims to identify characteristics of HIV-infected adults prior to diagnosis that could be used in a prediction model for early detection of patients for HIV testing in UK primary care.
Methods: The population of interest was adults aged ≥ 18 years in developed countries. The exposures were demographic, socio-economic or clinical characteristics associated with the outcome, laboratory confirmed HIV/AIDS infection. Observational studies with a comparator group were included in the systematic review. Electronic searches for articles from January 1995 to April 2016 were conducted on online databases of EMBASE, MEDLINE, The Cochrane Library and grey literature. Two reviewers selected studies for inclusion. A checklist was developed for quality assessment, and a data extraction form was created to collate data from selected studies.
Results: Full-text screening of 429 articles identified 17 cohort and case-control studies, from 26,819 retrieved articles. Demographic and socio-economic characteristics associated with HIV infection included age, gender and measures of deprivation. Lifestyle choices identified were drug use, binge-drinking, number of lifetime partners and having a partner with risky behaviour. Eighteen clinical features and comorbid conditions identified in this systematic review are included in the 51 conditions listed in the British HIV Association guidelines. Additional clinical features and comorbid conditions identified but not specified in the guidelines included hyperlipidemia, hypertension, minor trauma and diabetes.
Conclusion: This systematic review consolidates existing scientific evidence on characteristics of HIV-infected individuals that could be used to inform decision making in prognostic model development. Further exploration of availability of some of the demographic and behavioural predictors of HIV, such as ethnicity, number of lifetime partners and partner characteristics, in primary care records will be required to determine whether they can be applied in the prediction model.
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http://dx.doi.org/10.1186/s13643-018-0744-3 | DOI Listing |
J Med Internet Res
January 2025
Department of Computer Science and Software Engineering, United Arab Emirates University, Al Ain, United Arab Emirates.
Background: Neuroimaging segmentation is increasingly important for diagnosing and planning treatments for neurological diseases. Manual segmentation is time-consuming, apart from being prone to human error and variability. Transformers are a promising deep learning approach for automated medical image segmentation.
View Article and Find Full Text PDFJ Med Internet Res
January 2025
Cancer Screening, American Cancer Society, Atlanta, GA, United States.
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Methods: We followed the 2020 PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and conducted a search of MEDLINE, PsycINFO, Embase, CINAHL, and Web of Science databases from August 2010 to April 2023.
J Med Internet Res
January 2025
School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
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Department of Prosthodontics, Crown & Bridge, and Oral Implantology, Sudha Rustagi College of Dental Sciences and Research, Faridabad, Haryana, India.
Background: There is an increasing body of literature associating edentulism with cognitive impairment. The aim of this systematic review was to summarize the available data, emphasizing the role of removable dental prostheses in preventing cognitive deterioration and promoting brain health in elderly individuals.
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Cien Saude Colet
January 2025
Departamento de Enfermagem, Universidade Federal de Sergipe. Aracaju SE Brasil.
This review aimed to identify the impact of the ECHO® model on monitoring people diagnosed with diabetes mellitus. It followed the Joanna Briggs Institute and the PRISMA-ScR Checklist. The search was conducted in the Cochrane Library, Embase, Virtual Health Library, PubMed/MEDLINE, Scopus, and Web of Science databases.
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