RESUMO
We seek to address current limitations of forensic risk assessments by introducing the first mobile, self-scoring, risk assessment software that relies on neurocognitive testing to predict reoffense. This assessment, run entirely on a tablet, measures decision-making via a suite of neurocognitive tests in less than 30 minutes. The software measures several cognitive and decision-making traits of the user, including impulsivity, empathy, aggression, and several other traits linked to reoffending. Our analysis measured whether this assessment successfully predicted recidivism by testing probationers in a large urban city (Houston, TX, United States) from 2017 to 2019. To determine predictive validity, we used machine learning to yield cross-validated receiver-operator characteristics. Results gave a recidivism prediction value of 0.70, making it comparable to commonly used risk assessments. This novel approach diverges from traditional self-reporting, interview-based, and criminal-records-based approaches, and can also add a protective layer against bias, while strengthening model accuracy in predicting reoffense. In addition, subjectivity is eliminated and time-consuming administrative efforts are reduced. With continued data collection, this approach opens the possibility of identifying different levels of recidivism risk, by crime type, for any age, or gender, and seeks to steer individuals appropriately toward rehabilitative programs. Suggestions for future research directions are provided.
RESUMO
Despite the high prevalence of traumatic brain injuries (TBI), there are few rapid and straightforward tests to improve its assessment. To this end, we developed a tablet-based software battery ("BrainCheck") for concussion detection that is well suited to sports, emergency department, and clinical settings. This article is a study of the diagnostic accuracy of BrainCheck. We administered BrainCheck to 30 TBI patients and 30 pain-matched controls at a hospital Emergency Department (ED), and 538 healthy individuals at 10 control test sites. We compared the results of the tablet-based assessment against physician diagnoses derived from brain scans, clinical examination, and the SCAT3 test, a traditional measure of TBI. We found consistent distributions of normative data and high test-retest reliability. Based on these assessments, we defined a composite score that distinguishes TBI from non-TBI individuals with high sensitivity (83%) and specificity (87%). We conclude that our testing application provides a rapid, portable testing method for TBI.