The use of patient-reported outcomes (PROs) - standardized assessments by the patient of the impact of illness or therapy - has been met with increasing interest over the last years. Evidence generated from PROs is used not only to support claims in the drug regulatory process but is also used in political decision making and activities by the pharmaceutical industry. PROs can reflect the benefits of a treatment very well because the patient's quality of life is a central variable, and its preservation or improvement is certainly the main objective of any therapeutic intervention. The routine use of PROs is still hampered by several methodological problems. However, there are a number of advantages, such as improved interaction between physicians and patients and shared decision making, that may result in increased compliance.
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http://dx.doi.org/10.1007/s00347-008-1804-1 | DOI Listing |
J Law Med
November 2024
Associate Professor, La Trobe Law School, La Trobe University.
Risk assessment is an important component of judicial decision-making in many areas of the law. In Australia, those convicted of terrorist offences may be the subject of continued detention in prison or extended supervision in the community if there is an "unacceptable risk" of them committing future terrorism offences. Forensic psychologists and psychiatrists may provide evidence of risk through identifying and measuring risk factors with the aid of tools that use scales based on statistical or actuarial risk prediction.
View Article and Find Full Text PDFBMC Med Inform Decis Mak
January 2025
Department of Pathology and Laboratory Medicine, The Aga Khan University Hospital, Stadium Road, Karachi, 74800, Pakistan.
Background: Reference intervals (RIs) are crucial for distinguishing healthy from sick individuals and vary across age groups. Hemoglobinopathies are common in Pakistan, making the quantification of hemoglobin variants essential for screening. Direct RIs are established by measuring values from a healthy reference population, whereas indirect RIs, use statistical analysis of routine lab data to estimate values, making it feasible in settings where direct data is unavailable.
View Article and Find Full Text PDFJ Headache Pain
January 2025
Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea.
Inter-individual variability in symptoms and the dynamic nature of brain pathophysiology present significant challenges in constructing a robust diagnostic model for migraine. In this study, we aimed to integrate different types of magnetic resonance imaging (MRI), providing structural and functional information, and develop a robust machine learning model that classifies migraine patients from healthy controls by testing multiple combinations of hyperparameters to ensure stability across different migraine phases and longitudinally repeated data. Specifically, we constructed a diagnostic model to classify patients with episodic migraine from healthy controls, and validated its performance across ictal and interictal phases, as well as in a longitudinal setting.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Internal Medicine, 1st Faculty of Medicine Charles University, Military University Hospital, Prague, Czechia.
We assessed the diagnostic performance of the Narrow-Band Imaging (NBI) International Colorectal Endoscopic Classification (NICE) and the Japan NBI Expert Team classification (JNET) in predicting histological outcomes of advanced colorectal lesions. Additionally, we evaluated the sensitivity and positive predictive value (PPV) of the JNET and NICE classifications individually for high-grade lesions (including HGD adenomas, intramucosal carcinomas, and T1 carcinomas). This was a retrospective analysis of prospectively collected data, involving 211 patients (130 men, mean age 60 years) who underwent colonoscopy with endoscopic resection of advanced colorectal neoplasia (lesions ≥ 10 mm).
View Article and Find Full Text PDFSci Rep
January 2025
Department of Engineering, iHealth Labs, Sunnyvale, CA, 94085, United States.
Large language models (LLMs) are fundamentally transforming human-facing applications in the health and well-being domains: boosting patient engagement, accelerating clinical decision-making, and facilitating medical education. Although state-of-the-art LLMs have shown superior performance in several conversational applications, evaluations within nutrition and diet applications are still insufficient. In this paper, we propose to employ the Registered Dietitian (RD) exam to conduct a standard and comprehensive evaluation of state-of-the-art LLMs, GPT-4o, Claude 3.
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