The utility of the Victoria Symptom Validity Test (VSVT) as a performance validity test (PVT) has been primarily established using response accuracy scores. However, the degree to which response latency may contribute to accurate classification of performance invalidity over and above accuracy scores remains understudied. Therefore, this study investigated whether combining VSVT accuracy and response latency scores would increase predictive utility beyond use of accuracy scores alone. Data from a mixed clinical sample of 163 patients, who were administered the VSVT as part of a larger neuropsychological battery, were analyzed. At least four independent criterion PVTs were used to establish validity groups (121 valid/42 invalid). Logistic regression models examining each difficulty level revealed that all VSVT measures were useful in classifying validity groups, both independently and when combined. Individual predictor classification accuracy ranged from 77.9 to 81.6%, indicating acceptable to excellent discriminability across the validity indices. The results of this study support the value of both accuracy and latency scores on the VSVT to identify performance invalidity, although the accuracy scores had superior classification statistics compared to response latency, and mean latency indices provided no unique benefit for classification accuracy beyond dimensional accuracy scores alone.
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Sci Rep
January 2025
School of Information Engineering, Shandong Huayu University of Technology, Dezhou, 253000, China.
In order to reduce the number of parameters in the Chinese herbal medicine recognition model while maintaining accuracy, this paper takes 20 classes of Chinese herbs as the research object and proposes a recognition network based on knowledge distillation and cross-attention - ShuffleCANet (ShuffleNet and Cross-Attention). Firstly, transfer learning was used for experiments on 20 classic networks, and DenseNet and RegNet were selected as dual teacher models. Then, considering the parameter count and recognition accuracy, ShuffleNet was determined as the student model, and a new cross-attention mechanism was proposed.
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January 2025
School of Mechanical Engineering, College of Engineering, University of Tehran, Tehran, Iran.
This paper introduces a novel method for spleen segmentation in ultrasound images, using a two-phase training approach. In the first phase, the SegFormerB0 network is trained to provide an initial segmentation. In the second phase, the network is further refined using the Pix2Pix structure, which enhances attention to details and corrects any erroneous or additional segments in the output.
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January 2025
Department of Orthopedic Surgery, Chang Gung Memorial Hospital, No. 5, Fuxing St., Guishan Dist, Linkou, Taoyuan, 33305, Taiwan.
Objective: To investigate the predictive ability of the MRI-based vertebral bone quality (VBQ) score for pedicle screw loosening following instrumented transforaminal lumbar interbody fusion (TLIF).
Methods: Data from patients who have received one or two-level instrumented TLIF from February 2014 to March 2015 were retrospectively collected. Pedicle screw loosening was diagnosed when the radiolucent zone around the screw exceeded 1 mm in plain radiographs.
Sci Rep
January 2025
Research Innovation and Entrepreneurship Unit, University of Buraimi, 512, Buraimi, Oman.
Skin diseases impact millions of people around the world and pose a severe risk to public health. These diseases have a wide range of effects on the skin's structure, functionality, and appearance. Identifying and predicting skin diseases are laborious processes that require a complete physical examination, a review of the patient's medical history, and proper laboratory diagnostic testing.
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January 2025
Department of Hospital Pathology, College of Medicine, The Catholic University of Korea, 222 Banpo-daero, Seocho-gu, Seoul, 06591, Korea.
Recently, as the number of cancer patients has increased, much research is being conducted for efficient treatment, including the use of artificial intelligence in genitourinary pathology. Recent research has focused largely on the classification of renal cell carcinoma subtypes. Nonetheless, the broader categorization of renal tissue into non-neoplastic normal tissue, benign tumor and malignant tumor remains understudied.
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