Impaired procedural learning has been suggested as a possible cause of developmental dyslexia (DD) and developmental language disorder (DLD). We evaluate this theory by performing a series of meta-analyses on evidence from the six procedural learning tasks that have most commonly been used to test this theory: the serial reaction time, Hebb learning, artificial grammar and statistical learning, weather prediction, and contextual cuing tasks. Studies using serial reaction time and Hebb learning tasks yielded small group deficits in comparisons between language impaired and typically developing controls ( = -.30 and -.32, respectively). However, a meta-analysis of correlational studies showed that the serial reaction time task was not a reliable correlate of language-related ability in unselected samples ( = .03). Larger group deficits were, however, found in studies using artificial grammar and statistical learning tasks ( = -.48) and the weather prediction task ( = -.63). Possible reasons for the discrepancy in results from different tasks that all purportedly measure procedural learning are highlighted. We conclude that current data do not provide an adequate test of the theory that a generalized procedural learning deficit is a causal risk factor for developmental dyslexia or developmental language disorder. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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Int J Med Inform
January 2025
School of Geography and the Environment, University of Oxford, South Parks Road, Oxford OX1 3QY, United Kingdom. Electronic address:
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View Article and Find Full Text PDFInt J Med Inform
January 2025
Rheumatology and Allergy Clinical Epidemiology Research Center and Division of Rheumatology, Allergy, and Immunology, and Mongan Institute, Department of Medicine, Massachusetts General Hospital Boston MA USA. Electronic address:
Background: ANCA-associated vasculitis (AAV) is a rare but serious disease. Traditional case-identification methods using claims data can be time-intensive and may miss important subgroups. We hypothesized that a deep learning model analyzing electronic health records (EHR) can more accurately identify AAV cases.
View Article and Find Full Text PDFClin Exp Optom
January 2025
Division of Pharmacy and Optometry, University of Manchester, Manchester, UK.
Clinical Relevance: Interprofessional education and collaborative working are known to improve patient outcomes. The evidence to support this approach in optometry is lacking.
Background: There is no published evidence into the effectiveness of interprofessional education for pharmacy and optometry students.
JMIR Cancer
January 2025
Division of Radiology and Biomedical Engineering, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Background: The application of natural language processing in medicine has increased significantly, including tasks such as information extraction and classification. Natural language processing plays a crucial role in structuring free-form radiology reports, facilitating the interpretation of textual content, and enhancing data utility through clustering techniques. Clustering allows for the identification of similar lesions and disease patterns across a broad dataset, making it useful for aggregating information and discovering new insights in medical imaging.
View Article and Find Full Text PDFRadiol Med
January 2025
Department of Radiology, University Hospital, LMU Munich, Marchioninistr. 15, 81377, Munich, Germany.
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