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The present study aims to evaluate the classification accuracy and resistance to coaching of the Inventory of Problems-29 (IOP-29) and the IOP-Memory (IOP-M) with a Spanish sample of patients diagnosed with mild traumatic brain injury (mTBI) and healthy participants instructed to feign. Using a simulation design, 37 outpatients with mTBI (clinical control group) and 213 non-clinical instructed feigners under several coaching conditions completed the Spanish versions of the IOP-29, IOP-M, Structured Inventory of Malingered Symptomatology, and Rivermead Post Concussion Symptoms Questionnaire. The IOP-29 discriminated well between clinical patients and instructed feigners, with an excellent classification accuracy for the recommended cutoff score (FDS ≥ .50; sensitivity = 87.10% for coached group and 89.09% for uncoached; specificity = 95.12%). The IOP-M also showed an excellent classification accuracy (cutoff ≤ 29; sensitivity = 87.27% for coached group and 93.55% for uncoached; specificity = 97.56%). Both instruments proved to be resistant to symptom information coaching and performance warnings. The results confirm that both of the IOP measures offer a similarly valid but different perspective compared to SIMS when assessing the credibility of symptoms of mTBI. The encouraging findings indicate that both tests are a valuable addition to the symptom validity practices of forensic professionals. Additional research in multiple contexts and with diverse conditions is warranted.
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http://dx.doi.org/10.1080/13854046.2023.2249171 | DOI Listing |
BMC Oral Health
September 2025
Oral and Maxillofacial Radiology Department, Cairo university, Cairo, Egypt.
Aim: The purpose of this study was to assess the accuracy of a customized deep learning model based on CNN and U-Net for detecting and segmenting the second mesiobuccal canal (MB2) of maxillary first molar teeth on cone beam computed tomography (CBCT) scans.
Methodology: CBCT scans of 37 patients were imported into 3D slicer software to crop and segment the canals of the mesiobuccal (MB) root of the maxillary first molar. The annotated data were divided into two groups: 80% for training and validation and 20% for testing.
Lipids Health Dis
September 2025
Department of Gastroenterology, Weifang People's Hospital, The First Affiliated Hospital of Shandong Second Medical University, 151 Guangwen Street, Weifang, Shandong, 261000, China.
Background: Current scoring systems for hypertriglyceridaemia-induced acute pancreatitis (HTG-AP) severity are few and lack reliability. The present work focused on screening predicting factors for HTG-SAP, then constructing and validating the visualization model of HTG-AP severity by combining relevant metabolic indexes.
Methods: Between January 2020 and December 2024, retrospective clinical information for HTG-AP inpatients from Weifang People's Hospital was examined.
BMC Psychiatry
September 2025
Department of Cognitive Neuroscience, Faculty of Biology, Bielefeld University, Bielefeld, Germany.
Obsessive-compulsive disorder (OCD) is a chronic and disabling condition affecting approximately 3.5% of the global population, with diagnosis on average delayed by 7.1 years or often confounded with other psychiatric disorders.
View Article and Find Full Text PDFBMC Musculoskelet Disord
September 2025
Department of Clinical Sciences at Danderyds Hospital, Department of Orthopedic Surgery, Karolinska Institutet, Stockholm, 182 88, Sweden.
Background: This study evaluates the accuracy of an Artificial Intelligence (AI) system, specifically a convolutional neural network (CNN), in classifying elbow fractures using the detailed 2018 AO/OTA fracture classification system.
Methods: A retrospective analysis of 5,367 radiograph exams visualizing the elbow from adult patients (2002-2016) was conducted using a deep neural network. Radiographs were manually categorized according to the 2018 AO/OTA system by orthopedic surgeons.
Environ Monit Assess
September 2025
Institute of Earth Sciences, Southern Federal University, Rostov-On-Don, Russia.
Sustainable urban development requires actionable insights into the thermal consequences of land transformation. This study examines the impact of land use and land cover (LULC) changes on land surface temperature (LST) in Ho Chi Minh city, Vietnam, between 1998 and 2024. Using Google Earth Engine (GEE), three machine learning algorithms-random forest (RF), support vector machine (SVM), and classification and regression tree (CART)-were applied for LULC classification.
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