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Introduction: "Dizziness" is a common complaint in clinical practice that can occur with anyone. However, since the symptom is caused by a wide range of disorders, a general clinician usually faces some difficulty to detect the cause.
Objective: This study aimed to formulate and validate a simple instrument that can be used to screen and predict the most likely cause of dizziness in Thai outpatients.
Methods: This study was divided into two phases. Phase I included 41 patients diagnosed with common causes of dizziness to determine the algorithm and construct the "structural algorithm questionnaire version 1". In addition, to test and retest its content validity and reliability until the instrument had an acceptable level of both. Phase II of the study pertained to evaluating its accuracy in clinical trials, 150 patients with dizziness had a face-to-face interview while they were waiting for their medical appointment.
Results: The degree of agreement between the algorithm results and clinical diagnoses was within an acceptable level (κ = 0.69). Therefore, this algorithm was used to construct the structural algorithm questionnaire version 1. The content validity of the structural algorithm questionnaire version 1 evaluated by seven experts. The content validity index values of the questionnaire ranged from 0.71 to 1.0. The Cohen's kappa coefficient (κ) of intra-rater reliability of the structural algorithm questionnaire version 1 was 0.71. In clinical trials, 150 patients with dizziness had a face-to-face interview while they were waiting for their appointment. The overall agreement between their questionnaire responses and final diagnoses by specialists showed a moderate degree of clinical accuracy (κ = 0.55).
Conclusions: The structural algorithm questionnaire version 1 had a well-developed design and acceptable quality pertaining to both validity and reliability. It might be used to differentiate the cause of dizziness between vestibular and non-vestibular disorders, especially of outpatients with dizziness symptoms.
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http://dx.doi.org/10.1016/j.bjorl.2021.05.007 | DOI Listing |
JMIR Res Protoc
September 2025
State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China.
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View Article and Find Full Text PDFJMIR Cancer
September 2025
Cancer Patients Europe, Rue de l'Industrie 24, Brussels, 1000, Belgium.
Background: Breast cancer is the most common cancer among women and a leading cause of mortality in Europe. Early detection through screening reduces mortality, yet participation in mammography-based programs remains suboptimal due to discomfort, radiation exposure, and accessibility issues. Thermography, particularly when driven by artificial intelligence (AI), is being explored as a noninvasive, radiation-free alternative.
View Article and Find Full Text PDFJ Bras Pneumol
September 2025
. Divisão de Pneumologia, Escola Paulista de Medicina, Universidade Federal de São Paulo, São Paulo (SP) Brasil.
Objective: To evaluate the quality of ChatGPT answers to asthma-related questions, as assessed from the perspectives of asthma specialists and laypersons.
Methods: Seven asthma-related questions were asked to ChatGPT (version 4) between May 3, 2024 and May 4, 2024. The questions were standardized with no memory of previous conversations to avoid bias.
Hematology
December 2025
Department of Pediatrics, The Affiliated Hospital of Guizhou Medical University, Guiyang, Guizhou Province, People's Republic of China.
Background: Childhood leukaemia remains a major global health challenge and its impact varies significantly by region. Understanding the patterns of incidence, mortality, prevalence, and disability-adjusted life years (DALYs) is crucial for crafting effective public health initiatives and enhancing care outcomes, especially in regions with constrained resources.
Methods: This study evaluates the worldwide, regional, and country-specific effects of childhood leukemia between 1990 and 2021, leveraging data from the Global Burden of Disease (GBD) initiative.
Ren Fail
December 2025
Department of Rehabilitation Medicine, Shanghai University of Medicine and Health Sciences Affiliated Zhoupu Hospital, Shanghai, China.
Background: Depression is a common mental disorder in hemodialysis patients. The present study aimed to identify subgroups of patients receiving hemodialysis based on depression and explore the influencing factors in a multicenter hemodialysis population in China.
Methods: A total of 1,090 hemodialysis patients (682 men, mean aged 61.