Objective: The Structured Inventory of Malingered Symptomatology (SIMS; Widows & Smith, 2005) is a 75-item self-report measure intended to screen for potentially feigned symptoms of mental illness and/or cognitive impairment. We investigated the classification accuracy of 2 new detection scales (Rare Symptoms [RS] and Symptom Combinations [SC]) developed by Rogers, Robinson, and Gillard (2014) that appeared useful in identifying simulated mental disorder in their derivation sample of psychiatric inpatients.
Hypothesis: We hypothesized that the rates of classification accuracy Rogers et al. reported for these 2 scales would generalize to other samples in which the utility of the SIMS previously has been investigated.
Method: We computed RS and SC scores from archival SIMS data collected as part of 3 research projects investigating malingering detection methods: (a) general population prison inmates and inmates in a prison psychiatric unit receiving treatment for mental disorder (N = 115), (b) college students (N = 196), and (3) community-dwelling adults (N = 48).
Results: Results supported the global classification accuracy of RS and SC but the suggested cut-score for both scales (>6) produced poor sensitivity. Lower potential cut-offs did, however, improve sensitivity to feigning somewhat while not excessively diminishing specificity.
Conclusion: These results emphasize the importance of generalizability research when investigating the clinical utility of forensic mental health assessment methods, particularly specific decision rules used to classify individuals into discrete categories. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1037/lhb0000361 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
To achieve carbon neutrality, solar photovoltaic (PV) in China has undergone enormous development over the past few years. PV datasets with high accuracy and fine temporal span are crucial to assess the corresponding carbon reductions. In this study, we employed the random forest classifier to extract PV installations throughout China in 2015 and 2020 using Landsat-8 imagery in Google Earth Engine.
View Article and Find Full Text PDFDevelopment and Validation of a Nomogram Based on Multiparametric MRI for Predicting Lymph Node Metastasis in Endometrial Cancer: A Retrospective Cohort Study.
Acad Radiol
December 2024
Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China (Y.T., Y.W., Y.Y., X.Q., Y.H., J.L.); Key Laboratory of Early Prevention and Treatment for Regional High Frequency Tumor (Guangxi Medical University), Ministry of Education, Nanning 530021, Guangxi Zhuang Autonomous Region, PR China (J.L.). Electronic address:
Rationale And Objectives: To develop a radiomics nomogram based on clinical and magnetic resonance features to predict lymph node metastasis (LNM) in endometrial cancer (EC).
Materials And Methods: We retrospectively collected 308 patients with endometrial cancer (EC) from two centers. These patients were divided into a training set (n=155), a test set (n=67), and an external validation set (n=86).
Endoscopic ultrasound-guided fine-needle aspiration and fine-needle biopsy in the diagnosis of gastrointestinal lymphomas.
J Am Soc Cytopathol
December 2024
Department of Pathology and Cell Biology, Columbia University Medical Center, New York Presbyterian Hospital, New York, New York.
Introduction: The role of endoscopic ultrasound-guided fine-needle aspiration and fine-needle biopsy (EUS-FNA/B) in the clinical management of gastrointestinal lymphoma has not been extensively studied. This study investigates the use of EUS-FNA/B in the diagnosis of first-time and recurrent gastrointestinal lymphomas at a large academic institution.
Materials And Methods: A total of 40 patients who had final diagnosis of lymphoma according to the World Health Organization (WHO) classification of tumors of hematopoietic lymphoid tissues who underwent EUS-FNA/B were included in the study.
Predicting lack of clinical improvement following varicose vein ablation using machine learning.
J Vasc Surg Venous Lymphat Disord
December 2024
Department of Surgery, University of Toronto, Canada; Division of Vascular Surgery, St. Michael's Hospital, Unity Health Toronto, Canada; Institute of Medical Science, University of Toronto, Canada; Temerty Centre for Artificial Intelligence Research and Education in Medicine (T-CAIREM), University of Toronto, Canada; Li Ka Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, Canada; Department of Surgery, King Faisal Specialist Hospital and Research Center, Saudi Arabia. Electronic address:
Objective: Varicose vein ablation is generally indicated in patients with active/healed venous ulcers. However, patient selection for intervention in individuals without venous ulcers is less clear. Tools that predict lack of clinical improvement (LCI) following vein ablation may help guide clinical decision-making but remain limited.
View Article and Find Full Text PDFFusion of transfer learning with nature-inspired dandelion algorithm for autism spectrum disorder detection and classification using facial features.
Sci Rep
December 2024
Department of Electronics, Information and Communication Engineering, Kangwon National University, Samcheok, 25913, Republic of Korea.
Autism spectrum disorder (ASD) is a neurologic disorder considered to cause discrepancies in physical activities, social skills, and cognition. There is no specific medicine for treating this disorder; early intervention is critical to improving brain function. Additionally, the lack of a clinical test for detecting ASD makes diagnosis challenging.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!