This study investigates the predictive validity of two risk instruments for stalking, the Guidelines for Stalking Assessment and Management (SAM) and the Stalking Risk Profile (SRP), in a sample of 86 forensic psychiatric patients. We compare these tools against a well-validated violence risk assessment measure (Historical, Clinical, Risk Management-20, Version 3 (HCR-20V3)) for violent and stalking-related outcomes. Dynamic (mutable) components of each tool were rated at three annual intervals and revealed significant change across time. The HCR-20V3, SAM, and SRP measures showed comparable ability to classify those who recidivated with further stalking from those who did not (area under the curves = .72-.73, <001). Time-varying scores from the dynamic subscales of the HCR-20V3 and SAM contributed significantly to the prediction of stalking, whereas nonstalking violence was primarily forecast by the static (Historical) scale of the HCR-20V3. This suggests comparable validity of general violence and stalking risk tools for assessing the risk of stalking in forensic patients. Stalking-specific risk factors on the SAM and SRP will likely be of added clinical value in terms of tailoring risk management and treatment plans. Findings also emphasize the importance of attending to changes in risk status over time and incorporating time-sensitive methodologies into predictive models.
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http://dx.doi.org/10.29158/JAAPL.220110-22 | DOI Listing |
J Reprod Immunol
January 2025
Department of Chinese Medicine Rehabilitation, The First Affiliated Hospital of Guizhou University of Traditional Chinese Medicine, Guiyang 50001, China. Electronic address:
Clinical evidence increasingly suggests that traditional treatments for dysfunctional uterine bleeding (DUB) have limited success. In this study, blood samples from 10 DUB patients and 10 healthy controls were collected for transcriptome sequencing. Then, the differentially expressed genes (DEGs) were screened and crossed with the DUB-related module genes to obtain the target genes.
View Article and Find Full Text PDFObjectives: To determine and compare the diagnostic accuracy of imaging tests for the prediction of RA progression in people with inflammatory joint pain or CSA.
Methods: We searched MEDLINE, Embase and Web of Science from 1987 to March 2024. Studies evaluating any imaging tests in participants with inflammatory joint pain or CSA, without clinical synovitis were eligible.
Environ Monit Assess
January 2025
Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Shollinganallur, Chennai, India.
Municipal waste classification is significant for effective recycling and waste management processes that involve the classification of diverse municipal waste materials such as paper, glass, plastic, and organic matter using diverse techniques. Yet, this municipal waste classification process faces several challenges, such as high computational complexity, more time consumption, and high variability in the appearance of waste caused by variations in color, type, and degradation level, which makes an inaccurate waste classification process. To overcome these challenges, this research proposes a novel Channel and Spatial Attention-Based Multiblock Convolutional Network for accurately classifying municipal waste that utilizes a unique attention mechanism for enhancing feature learning and waste classification accuracy.
View Article and Find Full Text PDFObjective: The objective of this research was to devise and authenticate a predictive model that employs CT radiomics and deep learning methodologies for the accurate prediction of synchronous distant metastasis (SDM) in clear cell renal cell carcinoma (ccRCC).
Methods: A total of 143 ccRCC patients were included in the training cohort, and 62 ccRCC patients were included in the validation cohort. The CT images from all patients were normalized, and the tumor regions were manually segmented via ITK-SNAP software.
Discov Oncol
January 2025
Second Department of Oncology, The Second Hospital of Hebei Medical University, Shijiazhuang, 050000, Hebei, China.
Introduction: We conducted a panoramic analysis of GBN5 expression and prognosis in 33 cancers, aiming to deepen the systematic understanding of GBN5 in cancer.
Materials And Methods: We employed a multi-omics approach, including transcriptomic, genomic, proteomic, single-cell cytomic, spatial transcriptomic, and genomic data, to explore the prognostic value and potential oncogenic mechanisms of GBN5 across pan-cancers from multiple perspectives.
Results: We found that GBN5 was differentially expressed in multiple tumors and showed early diagnostic value.
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