This article reviews the existing literature on the diagnostic accuracy of two grammatical accuracy measures for differentiating children with and without language impairment (LI) at preschool and early school age based on language samples. The first measure, the finite verb morphology composite (FVMC), is a narrow grammatical measure that computes children's overall accuracy of four verb tense morphemes. The second measure, percent grammatical utterances (PGU), is a broader grammatical measure that computes children's accuracy in producing grammatical utterances. The extant studies show that FVMC demonstrates acceptable (i.e., 80 to 89% accurate) to good (i.e., 90% accurate or higher) diagnostic accuracy for children between 4;0 (years;months) and 6;11 in conversational or narrative samples. In contrast, PGU yields acceptable to good diagnostic accuracy for children between 3;0 and 8;11 regardless of sample types. Given the diagnostic accuracy shown in the literature, we suggest that FVMC and PGU can be used as one piece of evidence for identifying children with LI in assessment when appropriate. However, FVMC or PGU should not be used as therapy goals directly. Instead, when children are low in FVMC or PGU, we suggest that follow-up analyses should be conducted to determine the verb tense morphemes or grammatical structures that children have difficulty with.
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http://dx.doi.org/10.1055/s-0036-1580740 | DOI Listing |
Eur Stroke J
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Department of Neurology, University Hospital RWTH Aachen, Aachen, Germany.
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Nano Lett
January 2025
Key Laboratory of Clinical Laboratory Diagnostics (Chinese Ministry of Education), College of Laboratory Medicine, Chongqing Medical University, Chongqing 400016, P. R. China.
Logical analysis of multiple-miRNA expression information and immediate output of diagnostic results facilitates early cancer detection. In this work, we constructed an isothermal molecular classifier capable of performing computations on multiple miRNAs and directly providing diagnosis results. First, we developed linear-after-the-exponential rolling circle amplification (LATE-RCA), a nearly linear isothermal amplification that does not destroy the original quantitative information about miRNAs.
View Article and Find Full Text PDFHeliyon
January 2025
Information Technology Department, Technical College of Informatics-Akre, Akre University for Applied Sciences, Kurdistan Regain, Iraq.
Deep Learning (DL) has significantly contributed to the field of medical imaging in recent years, leading to advancements in disease diagnosis and treatment. In the case of Diabetic Retinopathy (DR), DL models have shown high efficacy in tasks such as classification, segmentation, detection, and prediction. However, DL model's opacity and complexity lead to errors in decision-making, particularly in complex cases, making it necessary to estimate the model's uncertainty in predictions.
View Article and Find Full Text PDFFront Oncol
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Gynecologic Oncology, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.
Background: Ovarian cancer (OC) represents a common neoplasm within the female reproductive tract. The prognosis for patients diagnosed at advanced stages is unfavorable, primarily attributable to the absence of reliable screening markers for early detection. An elevated neutrophil-to-lymphocyte ratio (NLR) serves as an indicator of host inflammatory response and has been linked to poorer overall survival (OS) across various cancer types; however, its examination in OC remains limited.
View Article and Find Full Text PDFJ Anus Rectum Colon
January 2025
Department of Gastroenterological Surgery, Graduate School of Medical Sciences, Kumamoto University, Kumamoto, Japan.
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