Background: Endometriosis in adolescent girls is often diagnosed after a long delay. This diagnostic delay can be associated with more advanced stages of endometriosis and with a higher likelihood of fertility problems at a later age.
Material And Methods: A systematic review of literature and quality assessment was performed in order to identify questionnaires that were developed to identify adult women with endometriosis. Based on these questionnaires, specific questions that had been reported to be predictive for endometriosis were selected and included in a newly composed questionnaire with the aim to identify adolescents at risk of developing endometriosis.
Results: Based on the literature, we identified 5 questionnaires developed to identify adult women with endometriosis; this questionnaire contained 6 questions that had been reported to be predictive for adult endometriosis. These questions query age of menarche, cycle duration, dysmenorrhea, pain descriptors, dyschezia and urinary symptoms and were combined into a new self-report questionnaire aimed to identify adolescents at risk to develop endometriosis.
Conclusion: We developed a self-report questionnaire aimed to identify adolescents at risk to develop endometriosis based on questions from self-report questionnaires that have been reported to identify adult women with endometriosis.
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http://dx.doi.org/10.1159/000452098 | DOI Listing |
Int J Health Plann Manage
December 2024
Centre for Global Chronic Conditions, London School of Hygiene & Tropical Medicine, London, UK.
Background: Reducing inequities in hypertension control among those affected in low- and middle-income countries requires person-centred health system responses based on a contextualised understanding of the choices and care pathways taken by those who rely on the services provided, particularly those from poor and marginalised communities. We examine patterns of care seeking and pathways followed by individuals with hypertension from low-income households in the Philippines and Malaysia. This study aims to fill a significant gap in the literature by analysing the stages at which individuals make decisions that may affect the successful control of their blood pressure.
View Article and Find Full Text PDFJ Autism Dev Disord
December 2024
Department of Psychology, University of Wyoming, Laramie, WY, USA.
Purpose: Autistic adults experience high rates of traumatic events and PTSD. However, little work has evaluated motor vehicle accident (MVA) related trauma symptoms. The goal of this brief report was to provide pilot data characterizing MVA-related peritraumatic reactions, trauma symptoms, and rates of PTSD diagnosis and mental health service use among Autistic compared to non-autistic adults.
View Article and Find Full Text PDFTranspl Infect Dis
December 2024
Department of Infectious Diseases and Immunology, Austin Health, Heidelberg, Australia.
Background: Identifying patients with latent tuberculosis infection (LTBI) is challenging. This is particularly true amongst immunocompromised hosts, in whom the diagnostic accuracy of available tests is limited. The authors evaluated the impact of routine pretransplant review by a transplant infectious diseases (TID) physician on LTBI screening in allogeneic hematopoietic stem cell transplant (alloHSCT) recipients.
View Article and Find Full Text PDFActa Diabetol
December 2024
Department of Endocrinology, General Hospital of Central Theater Command, Wuhan, Hubei, People's Republic of China.
Aims: There is a potential association between oxidative stress and the development of diabetic kidney disease (DKD). The Oxidative Balance Score (OBS), derived from dietary and lifestyle factors, acts as a comprehensive marker of oxidative stress. Research examining the relationship between OBS and DKD is scarce.
View Article and Find Full Text PDFJ Am Med Inform Assoc
December 2024
AI for Health Institute, Washington University in St Louis, St Louis, MO 63130, United States.
Objective: Early detection of surgical complications allows for timely therapy and proactive risk mitigation. Machine learning (ML) can be leveraged to identify and predict patient risks for postoperative complications. We developed and validated the effectiveness of predicting postoperative complications using a novel surgical Variational Autoencoder (surgVAE) that uncovers intrinsic patterns via cross-task and cross-cohort presentation learning.
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