Peer review plays a crucial role in accreditation and credentialing processes as it can identify outliers and foster a peer learning approach, facilitating error analysis and knowledge sharing. However, traditional peer review methods may fall short in effectively addressing the interpretive variability among reviewing and primary reading radiologists, hindering scalability and effectiveness. Reducing this variability is key to enhancing the reliability of results and instilling confidence in the review process. In this paper, we propose a novel statistical approach called "Bayesian Inter-Reviewer Agreement Rate" (BIRAR) that integrates radiologist variability. By doing so, BIRAR aims to enhance the accuracy and consistency of peer review assessments, providing physicians involved in quality improvement and peer learning programs with valuable and reliable insights. A computer simulation was designed to assign predefined interpretive error rates to hypothetical interpreting and peer-reviewing radiologists. The Monte Carlo simulation then sampled (100 samples per experiment) the data that would be generated by peer reviews. The performances of BIRAR and four other peer review methods for measuring interpretive error rates were then evaluated, including a method that uses a gold standard diagnosis. Application of the BIRAR method resulted in 93% and 79% higher relative accuracy and 43% and 66% lower relative variability, compared to "Single/Standard" and "Majority Panel" peer review methods, respectively. Accuracy was defined by the median difference of Monte Carlo simulations between measured and pre-defined "actual" interpretive error rates. Variability was defined by the 95% CI around the median difference of Monte Carlo simulations between measured and pre-defined "actual" interpretive error rates. BIRAR is a practical and scalable peer review method that produces more accurate and less variable assessments of interpretive quality by accounting for variability within the group's radiologists, implicitly applying a standard derived from the level of consensus within the group across various types of interpretive findings.
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http://dx.doi.org/10.1007/s10278-024-00971-9 | DOI Listing |
Front Public Health
December 2024
Department of Ophtalmology, Medical School, University of Pécs, Pécs, Hungary.
Background: Recent studies suggest that increased digital technology usage could be a factor in the rising occurrence and severity of headache episodes. The purpose of this cross-sectional study was to determine whether the severity of primary headaches (migraine and tension-type headache) is associated with problematic internet use taking many covariates into account.
Methods: We conducted an online cross-sectional survey using a quantitative, descriptive questionnaire, targeting university students enrolled in correspondence courses, aged 18 to 65.
Front Public Health
December 2024
Department of Data Integration and Analysis, Statens Serum Institut, Copenhagen, Denmark.
Except for a few countries, comprehensive all-cause surveillance for bacteremia is not part of mandatory routine public health surveillance. We argue that time has come to include automated surveillance for bacteremia in the national surveillance systems, and explore diverse approaches and challenges in establishing bacteremia monitoring. Assessed against proposed criteria, surveillance for bacteremia should be given high priority.
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December 2024
Institute of Psychology, University of Debrecen, Debrecen, Hungary.
Introduction: The positive impact of youth sport on physical, mental and social health has been highlighted in several research which reinforces further investigations concerning the reasons for dropout of athletes. As one of the most emergent difficulties in youth sports is to prevent athletes from dropping out, it is important to explore what factors play important part in this process. The purpose of this study was to identify barriers and challenges related to sport persistence and dropout.
View Article and Find Full Text PDFFront Public Health
December 2024
Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore.
Objective: To characterize the public conversations around long COVID, as expressed through X (formerly Twitter) posts from May 2020 to April 2023.
Methods: Using X as the data source, we extracted tweets containing #long-covid, #long_covid, or "long covid," posted from May 2020 to April 2023. We then conducted an unsupervised deep learning analysis using Bidirectional Encoder Representations from Transformers (BERT).
Front Neurosci
December 2024
Institute of Physiology, RG Neurophysiology and Optogenetics, Medical Faculty, Otto-von-Guericke-University, Magdeburg, Germany.
Cognitive function in healthy aging and neurodegenerative diseases like Alzheimer's disease (AD) correlates to olfactory performance. Aging and disease progression both show marked olfactory deficits in humans and rodents. As a clear understanding of what causes olfactory deficits is still missing, research on this topic is paramount to diagnostics and early intervention therapy.
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