Context: The diagnostic accuracy of tests used to diagnose GH deficiency (GHD) in adults is unclear.
Objective: We conducted a systematic review and meta-analysis of studies that provided data on the available diagnostic tests.
Data Sources: We searched electronic databases (MEDLINE, EMBASE, Cochrane CENTRAL, Web of Sciences, and Scopus) through April 2011.
Study Selection: Review of reference lists and contact with experts identified additional candidate studies. Reviewers, working independently and in duplicate, determined study eligibility.
Data Extraction: reviewers, working independently and in duplicate, determined the methodological quality of studies and collected descriptive, quality, and outcome data.
Data Synthesis: Twenty-three studies provided diagnostic accuracy data; none provided patient outcome data. Studies had fair methodological quality, used several reference standards, and included over 1100 patients. Several tests based on direct or indirect stimulation of GH release were associated with good diagnostic accuracy, although most were assessed in one or two studies decreasing the strength of inference due to small sample size. Serum levels of GH or IGF1 had low diagnostic accuracy. Pooled sensitivity and specificity of the two most commonly used stimulation tests were found to be 95 and 89% for the insulin tolerance test and 73 and 81% for the GHRH+arginine test respectively. Meta-analytic estimates for accuracy were associated with substantial heterogeneity.
Conclusion: Several tests with reasonable diagnostic accuracy are available for the diagnosis of GHD in adults. The supporting evidence, however, is at high risk of bias (due to heterogeneity, methodological limitations, and imprecision).
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http://dx.doi.org/10.1530/EJE-11-0476 | DOI Listing |
Nanomedicine (Lond)
January 2025
Department of Ultrasound, Yantaishan Hospital, Binzhou Medical University, Yantai, Shandong, China.
With the rapid development of nanotechnology, nanoultrasonography has emerged as a promising medical imaging technique that demonstrates significant potential in the diagnosis and treatment of gastrointestinal (GI) diseases. This review discusses the applications of nanoultrasonography in the gastrointestinal field, including improvements in imaging resolution, diagnostic accuracy, latest research findings, and prospects for clinical application. By analyzing existing literature, we explore the role of nanoultrasonography in enhancing imaging resolution, enabling targeted drug delivery, and improving therapeutic outcomes, thereby providing a reference for future research directions.
View Article and Find Full Text PDFClin Implant Dent Relat Res
February 2025
SEMRUK Technology Inc., Cumhuriyet Teknokent, Sivas, Turkiye.
Objectives: This study aimed to develop an artificial intelligence (AI)-based deep learning model for the detection and numbering of dental implants in panoramic radiographs. The novelty of this model lies in its ability to both detect and number implants, offering improvements in clinical decision support for dental implantology.
Materials And Methods: A retrospective dataset of 32 585 panoramic radiographs, collected from patients at Sivas Cumhuriyet University between 2014 and 2024, was utilized.
Eur Heart J Digit Health
January 2025
Division of Cardiovascular Medicine, Department of Clinical Sciences, Danderyd Hospital, Karolinska Institutet, SE-182 88 Stockholm, Sweden.
Aims: A simplified version of the history, electrocardiogram, age, risk factors, troponin (HEART) score, excluding troponin, has been proposed to rule-out major adverse cardiac events (MACEs). Computerized history taking (CHT) provides a systematic and automated method to obtain information necessary to calculate the HEAR score. We aimed to evaluate the efficacy and diagnostic accuracy of CHT in calculating the HEAR score for predicting MACE.
View Article and Find Full Text PDFEur Heart J Digit Health
January 2025
Cardiovascular Center, Tufts Medical Center, 800 Washington Street, Boston, MA 02111, USA.
Aims: This study evaluates the performance of OpenAI's latest large language model (LLM), Chat Generative Pre-trained Transformer-4o, on the Adult Clinical Cardiology Self-Assessment Program (ACCSAP).
Methods And Results: Chat Generative Pre-trained Transformer-4o was tested on 639 ACCSAP questions, excluding 45 questions containing video clips, resulting in 594 questions for analysis. The questions included a mix of text-based and static image-based [electrocardiogram (ECG), angiogram, computed tomography (CT) scan, and echocardiogram] formats.
Eur Heart J Digit Health
January 2025
Hunan Key Laboratory of Biomedical Nanomaterials and Devices, Hunan University of Technology, No. 88 West Taishan Road, Zhuzhou 412007, Hunan, China.
Aims: The electrocardiogram (ECG) is the primary method for diagnosing atrial fibrillation (AF), but interpreting ECGs can be time-consuming and labour-intensive, which deserves more exploration.
Methods And Results: We collected ECG data from 6590 patients as YY2023, classified as Normal, AF, and Other. Convolutional Neural Network (CNN), bidirectional Long Short-Term Memory (BiLSTM), and Attention construct the AF recognition model CNN BiLSTM Attention-Atrial Fibrillation (CLA-AF).
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