This study developed regression algorithms for estimating IQ scores using the Canadian WAIS-III norms. Participants were the Canadian WAIS-III standardization sample (n = 1,105). The sample was randomly divided into two groups (Development and Validation groups). The Development group was used to generate 12 regression algorithms for FSIQ and three algorithms each for VIQ and PIQ. Algorithms combined demographic variables with WAIS-III subtest raw scores. The algorithms accounted for 48-78% of the variance in FSIQ, 70-71% in VIQ, and 45-55% in PIQ. In the Validation group, the majority of the sample had predicted IQs that fell within a 95% CI band (FSIQ=92-94%; VIQ=93-95%; PIQ=94-94%). These algorithms yielded reasonably accurate estimates of FSIQ, VIQ, and PIQ in this healthy adult population. It is anticipated that these algorithms will be useful as a means for estimating premorbid IQ scores in a clinical population. However, prior to clinical use, these algorithms must be validated for this purpose.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1016/j.acn.2005.06.004 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Itching tends to worsen at night in patients with itchy skin diseases, such as atopic dermatitis. Unconscious scratching during sleep can exacerbate symptoms, cause sleep disturbances, or reduce quality of life. Therefore, evaluating nocturnal scratching behaviour is important for better patient care.
View Article and Find Full Text PDFNetwork analysis of socioeconomic disparities and public transport in COVID-19 spread: a case study in northeast Brazil.
J Infect Dev Ctries
December 2024
Graduate Program in Health Sciences, Federal University of Sergipe, SE, Brazil.
Introduction: The coronavirus disease 2019 (COVID-19) pandemic has significantly impacted public transportation systems worldwide. In this study, we evaluated the rate of COVID-19 positivity and its associated factors among users of public transportation in socioeconomically disadvantaged regions of Brazil during the pre-vaccination phase of the pandemic.
Methodology: This ecological study, conducted in Aracaju city in Northeast Brazil, is a component of the TestAju Program.
HDN-DDI: a novel framework for predicting drug-drug interactions using hierarchical molecular graphs and enhanced dual-view representation learning.
BMC Bioinformatics
January 2025
School of Computer Science and Technology, University of Science and Technology of China, 443 Huangshan Road, Hefei, 230027, China.
Background: Drug-drug interactions (DDIs) especially antagonistic ones present significant risks to patient safety, underscoring the urgent need for reliable prediction methods. Recently, substructure-based DDI prediction has garnered much attention due to the dominant influence of functional groups and substructures on drug properties. However, existing approaches face challenges regarding the insufficient interpretability of identified substructures and the isolation of chemical substructures.
View Article and Find Full Text PDFPredicting bullying victimization among adolescents using the risk and protective factor framework: a large-scale machine learning approach.
BMC Public Health
January 2025
Statistics, Brigham Young University, Provo, 84602, Utah, USA.
Background: Bullying, encompassing physical, psychological, social, or educational harm, affects approximately 1 in 20 United States teens aged 12-18. The prevalence and impact of bullying, including online bullying, necessitate a deeper understanding of risk and protective factors to enhance prevention efforts. This study investigated the key risk and protective factors most highly associated with adolescent bullying victimization.
View Article and Find Full Text PDFAge group classification based on optical measurement of brain pulsation using machine learning.
Sci Rep
January 2025
Research Unit of Health Sciences and Technology, University of Oulu, Oulu, Finland.
Optical techniques, such as functional near-infrared spectroscopy (fNIRS), contain high potential for the development of non-invasive wearable systems for evaluating cerebral vascular condition in aging, due to their portability and ability to monitor real-time changes in cerebral hemodynamics. In this study, thirty-six healthy adults were measured by single channel fNIRS to explore differences between two age groups using machine learning (ML). The subjects, measured during functional magnetic resonance imaging (fMRI) at Oulu University Hospital, were divided into young (age ≤ 32) and elderly (age ≥ 57) groups.
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!