Introduction: In high-stakes assessment, the measurement precision of pass-fail decisions is of great importance. A concept for analyzing the measurement precision at the cut score is conditional reliability, which describes measurement precision for every score achieved in an exam. We compared conditional reliabilities in Classical Test Theory (CTT) and Item Response Theory (IRT) with a special focus on the cut score and potential factors influencing conditional reliability at the cut score.
Methods: We analyzed 32 multiple-choice exams from three Swiss medical schools comparing conditional reliability at the cut score in IRT and CCT. Additionally, we analyzed potential influencing factors such as the range of examinees' performance, year of study, and number of items using multiple regression.
Results: In CTT, conditional reliability was highest for very low and very high scores, whereas examinees with medium scores showed low conditional reliabilities. In IRT, the maximum conditional reliability was in the middle of the scale. Therefore, conditional reliability at the cut score was significantly higher in IRT compared with CTT. It was influenced by the range of examinees' performance and number of items. This influence was more pronounced in CTT.
Discussion: We found that conditional reliability shows inverse distributions and conclusions regarding the measurement precision at the cut score depending on the theory used. As the use of IRT seems to be more appropriate for criterion-oriented standard setting in the framework of competency-based medical education, our findings might have practical implications for the design and quality assurance of medical education assessments.
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http://dx.doi.org/10.1007/s40037-020-00586-0 | DOI Listing |
Brain Commun
January 2025
Department of Biological Sciences, Southern Methodist University, Dallas, TX 75275, USA.
Sudden unexpected death in epilepsy (SUDEP) is the leading cause of epilepsy-related death, likely stemming from seizure activity disrupting vital brain centres controlling heart and breathing function. However, understanding of SUDEP's anatomical basis and mechanisms remains limited, hampering risk evaluation and prevention strategies. Prior studies using a neuron-specific conditional knockout mouse model of SUDEP identified the primary importance of brain-driven mechanisms contributing to sudden death and cardiorespiratory dysregulation; yet, the underlying neurocircuits have not been identified.
View Article and Find Full Text PDFJ Hazard Mater
December 2024
Discipline of Chemistry, The University of Newcastle, University Drive, Newcastle, New South Whales 2308, Australia; School of Chemistry, Monash University, Wellington Road, Melbourne, Victoria 3800, Australia. Electronic address:
Microplastics are ubiquitous and appear to be harmful, however, the full extent to which these inflict harm has not been fully elucidated. Analysing environmental sample data is challenging, as the complexity in real data makes both automated and manual analysis either unreliable or time-consuming. To address challenges, we explored a dense feed-forward neural network (DNN) for classifying Fourier transform infrared (FTIR) spectroscopic data.
View Article and Find Full Text PDFCogn Neurodyn
December 2025
Department of Electronics and Communication Engineering, Karpagam College of Engineering, Coimbatore, Tamil Nadu 641032 India.
Cross subject Electroencephalogram (EEG) emotion recognition refers to the process of utilizing electroencephalogram signals to recognize and classify emotions across different individuals. It tracks neural electrical patterns, and by analyzing these signals, it's possible to infer a person's emotional state. The objective of cross-subject recognition is to create models or algorithms that can reliably detect emotions in both the same person and several other people.
View Article and Find Full Text PDFBrief Bioinform
November 2024
School of Artificial Intelligence, Jilin University, 3003 Qianjin Street, 130012 Changchun, China.
Accurate identification of causal genes for cancer prognosis is critical for estimating disease progression and guiding treatment interventions. In this study, we propose CPCG (Cancer Prognosis's Causal Gene), a two-stage framework identifying gene sets causally associated with patient prognosis across diverse cancer types using transcriptomic data. Initially, an ensemble approach models gene expression's impact on survival with parametric and semiparametric hazard models.
View Article and Find Full Text PDFDrug Alcohol Depend
January 2025
University of Miami Miller School of Medicine, Department of Public Health Sciences, United States.
Introduction: Prevalence estimates of opioid use disorder (OUD) at local levels are critical for public health planning and surveillance, yet largely unavailable across the US especially at the local county level.
Methods: We used a Bayesian evidence synthesis approach to estimate the prevalence of OUD for 57 counties across New York State for 2017-2019 and compare rates of OUD across counties as well as assess the extent of undiagnosed OUD. We developed a generative model to assess conditional probabilistic relations between different subgroups of the OUD population defined by diagnosis, treatment, and overdose fatality.
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