Background: Criterion-referenced interpretations of tests are highly necessary, which usually involves the difficult task of establishing cut scores. Contrasting with other Item Response Theory (IRT)-based standard setting methods, a non-judgmental approach is proposed in this study, in which Item Characteristic Curve (ICC) transformations lead to the final cut scores.
Method: eCat-Listening, a computerized adaptive test for the evaluation of English Listening, was administered to 1,576 participants, and the proposed standard setting method was applied to classify them into the performance standards of the Common European Framework of Reference for Languages (CEFR).
Results: The results showed a classification closely related to relevant external measures of the English language domain, according to the CEFR.
Conclusions: It is concluded that the proposed method is a practical and valid standard setting alternative for IRT-based tests interpretations.
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http://dx.doi.org/10.7334/psicothema2012.147 | DOI Listing |
JAMA Cardiol
January 2025
Ifakara Health Institute, Ifakara Branch, Ifakara, United Republic of Tanzania.
Importance: Hypertension is the primary cardiovascular risk factor in Africa. Recently revised World Health Organization guidelines recommend starting antihypertensive dual therapy; clinical efficacy and tolerability of low-dose triple combination remain unclear.
Objectives: To compare the effect of 3 treatment strategies on blood pressure control among persons with untreated hypertension in Africa.
J Vis Exp
January 2025
Department of Biology, Mount Saint Vincent University;
Zebrafish scales offer a variety of advantages for use in standard laboratories for teaching and research purposes. Scales are easily collected without the need for euthanasia, regenerate within a couple of weeks, and are translucent and small, allowing them to be viewed using a standard microscope. Zebrafish scales are especially useful in educational environments, as they provide a unique opportunity for students to engage in hands-on learning experiences, particularly in understanding cellular dynamics and in vitro culturing methods.
View Article and Find Full Text PDFEur J Nucl Med Mol Imaging
January 2025
Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Spitalgasse 23, Vienna, 1090, Austria.
Purpose: Advancements of deep learning in medical imaging are often constrained by the limited availability of large, annotated datasets, resulting in underperforming models when deployed under real-world conditions. This study investigated a generative artificial intelligence (AI) approach to create synthetic medical images taking the example of bone scintigraphy scans, to increase the data diversity of small-scale datasets for more effective model training and improved generalization.
Methods: We trained a generative model on Tc-bone scintigraphy scans from 9,170 patients in one center to generate high-quality and fully anonymized annotated scans of patients representing two distinct disease patterns: abnormal uptake indicative of (i) bone metastases and (ii) cardiac uptake indicative of cardiac amyloidosis.
Epilepsia
January 2025
Department of Clinical Neurosciences, University of Calgary, Calgary, Alberta, Canada.
Clinical practice guidelines (CPGs) and consensus-based recommendations (CBRs) require considerable effort, collaboration, and time-all within the constraints of finite resources. Professional societies, such as the International League Against Epilepsy (ILAE), must prioritize what topics and questions to address. Implementing evidence-based care remains a crucial challenge in clinical practice.
View Article and Find Full Text PDFMed Phys
January 2025
Deparment of Radiation Oncology, Duke University, Durham, North Carolina, USA.
Background: Stereotactic radiosurgery (SRS) is widely used for managing brain metastases (BMs), but an adverse effect, radionecrosis, complicates post-SRS management. Differentiating radionecrosis from tumor recurrence non-invasively remains a major clinical challenge, as conventional imaging techniques often necessitate surgical biopsy for accurate diagnosis. Machine learning and deep learning models have shown potential in distinguishing radionecrosis from tumor recurrence.
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