To compare different scoring algorithms for Pick-N multiple correct answer multiple-choice (MC) exams regarding test reliability, student performance, total item discrimination and item difficulty. Data from six 3rd year medical students' end of term exams in internal medicine from 2005 to 2008 at Munich University were analysed (1,255 students, 180 Pick-N items in total). Scoring Algorithms: Each question scored a maximum of one point. We compared: (a) Dichotomous scoring (DS): One point if all true and no wrong answers were chosen. (b) Partial credit algorithm 1 (PS(50)): One point for 100% true answers; 0.5 points for 50% or more true answers; zero points for less than 50% true answers. No point deduction for wrong choices. (c) Partial credit algorithm 2 (PS(1/m)): A fraction of one point depending on the total number of true answers was given for each correct answer identified. No point deduction for wrong choices. Application of partial crediting resulted in psychometric results superior to dichotomous scoring (DS). Algorithms examined resulted in similar psychometric data with PS(50) only slightly exceeding PS(1/m) in higher coefficients of reliability. The Pick-N MC format and its scoring using the PS(50) and PS(1/m) algorithms are suited for undergraduate medical examinations. Partial knowledge should be awarded in Pick-N MC exams.
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http://dx.doi.org/10.1007/s10459-010-9256-1 | DOI Listing |
BMC Med Inform Decis Mak
January 2025
QUEST Center for Responsible Research, Berlin Institute of Health at Charité Universitätsmedizin Berlin, Berlin, Germany.
Background: Machine learning (ML) is increasingly used to predict clinical deterioration in intensive care unit (ICU) patients through scoring systems. Although promising, such algorithms often overfit their training cohort and perform worse at new hospitals. Thus, external validation is a critical - but frequently overlooked - step to establish the reliability of predicted risk scores to translate them into clinical practice.
View Article and Find Full Text PDFJ Patient Rep Outcomes
January 2025
Department of Physical Medicine and Rehabilitation, University of Michigan, 1540 E. Hospital Dr, Ann Arbor, MI, 48109, USA.
Aims: This study aims to improve the interpretability and clinical utility of the COmprehensive Score for financial Toxicity-Functional Assessment of Chronic Illness Therapy (COST-FACIT) by identifying distinct financial toxicity classes in adults with diabetes.
Methods: Data included a sample of 600 adults with Type 1 or Type 2 diabetes and high A1c. Latent Class Analysis was used to identify subgroups of patients based on COST-FACIT score patterns.
Sci Rep
January 2025
Department of Emergency, The First Hospital of China Medical University, No. 155, Nanjing North Street, Heping District, Shenyang, 11001, China.
The study aimed to develop and validate a sepsis prediction model using structured electronic medical records (sEMR) and machine learning (ML) methods in emergency triage. The goal was to enhance early sepsis screening by integrating comprehensive triage information beyond vital signs. This retrospective cohort study utilized data from the MIMIC-IV database.
View Article and Find Full Text PDFSci Rep
January 2025
Scientific Affairs Department, Al-Mustaqbal University, Babylon, 51001, Iraq.
This study investigates the application of various neural network-based models for predicting temperature distribution in freeze drying process of biopharmaceuticals. For heat-sensitive biopharmaceutical products, freeze drying is preferred to prevent degradation of pharmaceutical compounds. The modeling framework is based on CFD (Computational Fluid Dynamics) and machine learning (ML).
View Article and Find Full Text PDFSci Rep
January 2025
Department of Information Technology Management, Faculty of Management Technology and Information System, Port Said University, Port Said, 42526, Egypt.
The Internet of Things (IoTs) has revolutionized cities, enabling them to become smarter. IoTs play an important role in monitoring the traffic cameras, roads, smart farming, connected vehicles, air quality, water level, humidity, and carbon dioxide pollution levels in city buildings. One of the major challenges of smart cities is the cyber threat to sensitive data.
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