Risk tools containing dynamic (potentially changeable) factors are routinely used to evaluate the recidivism risk of justice-involved individuals. Although frequent reassessments are recommended, there is little research on how the predictive accuracy of dynamic risk assessments changes over time. This study examined the extent to which predictive accuracy decreases over time for the ACUTE-2007 and the STABLE-2007 sexual recidivism risk tools. We used two independent samples of men on community supervision ( = 795; = 4,221). For all outcomes (sexual, violent, and any recidivism [including technical violations]), reassessments improved predictive accuracy, with the largest effects found for the most recent assessment (i.e., those closest in time prior to the recidivism event). Based on these results, we recommend that ACUTE-2007 assessments occur at least every 30 days and that the STABLE-2007 assessments occur every 6 months or after significant life changes (e.g., successful completion of treatment).
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http://dx.doi.org/10.1177/10731911231177227 | DOI Listing |
JCI Insight
January 2025
Medical Oncology Department, Research Institute for Medical Innovation, Radboud University Medical Center, Nijmegen, Netherlands.
Background: Previously, we demonstrated that changes in circulating tumor DNA (ctDNA) are promising biomarkers for early response prediction (ERP) to immune checkpoint inhibitors (ICI) in metastatic urothelial cancer (mUC). In this study, we investigated the value of whole blood immunotranscriptomics for ERP-ICI and integrated both biomarkers into a multimodal model to boost accuracy.
Methods: Blood samples of 93 patients were collected at baseline and after 2-6 weeks of ICI for ctDNA (N=88) and immunotranscriptome (N=79) analyses.
J Med Chem
January 2025
Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.
Retrosynthesis is a strategy to analyze the synthetic routes for target molecules in medicinal chemistry. However, traditional retrosynthesis predictions performed by chemists and rule-based expert systems struggle to adapt to the vast chemical space of real-world scenarios. Artificial intelligence (AI) has revolutionized retrosynthesis prediction in recent decades, significantly increasing the accuracy and diversity of predictions for target compounds.
View Article and Find Full Text PDFJAMA Oncol
January 2025
Department of Urology, Seoul National University Hospital, Seoul, Republic of Korea.
Importance: An accurate noninvasive biomarker test is needed for the early diagnosis of bladder cancer.
Objective: To evaluate the performance of a urinary DNA methylation test (PENK methylation) and compare its diagnostic accuracy with that of the nuclear matrix protein 22 (NMP22) test or urine cytology test.
Design, Setting, And Participants: In this prospective multicenter study at 10 sites in the Republic of Korea, individuals 40 years and older with hematuria undergoing cystoscopy within 3 months between March 11, 2022, and May 30, 2024, participated.
Trop Anim Health Prod
January 2025
Livestock Production and Management Section, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, Uttar Pradesh, 243 122, India.
Dry matter intake (DMI) determination is essential for effective management of meat goats, especially in optimizing feed utilization and production efficiency. Unfortunately, farmers often face challenges in accurately predicting DMI which leads to wastage of feed and an increase in the cost of production. This investigation aimed to predict DMI in Black Bengal goats by using body weight (BW), body condition score (BCS), average daily gain (ADG), and metabolic body weight (MBW) by applying an artificial neural network (ANN) model.
View Article and Find Full Text PDFEur J Nucl Med Mol Imaging
January 2025
Department of Nuclear Medicine, West China Hospital, Sichuan University, No.37, Guoxue Alley, Chengdu City, Sichuan Province, 610041, China.
Background: Pathological grade is a critical determinant of clinical outcomes and decision-making of follicular lymphoma (FL). This study aimed to develop a deep learning model as a digital biopsy for the non-invasive identification of FL grade.
Methods: This study retrospectively included 513 FL patients from five independent hospital centers, randomly divided into training, internal validation, and external validation cohorts.
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