We developed a multiple-form list learning test appropriate for use with the Greek population and generated norms for clinical and research use. This task, the Greek Verbal Learning Test (GVLT), was based on the California Verbal Learning Test. We administered the standard version (Form A) to a sample of 354 healthy individuals, as well as two alternative forms (B and C) to a subgroup of the initial sample. Performance on the three forms was equivalent, and each test presented excellent internal consistency. We found good sensitivity and specificity in the testãs (Form A) utility in differentiating individuals with schizophrenia (n = 50) and individuals with traumatic brain injury (n = 53) from healthy adults. A multiple regression analysis indicated that age, education and sex predicted performance. Regression-based norms are also provided. Taken together, these data provide preliminary support for the reliability and construct validity of the GVLT.

Download full-text PDF

Source
http://dx.doi.org/10.1093/arclin/acs099DOI Listing

Publication Analysis

Top Keywords

learning test
16
verbal learning
12
greek verbal
8
reliability construct
8
construct validity
8
test
5
development greek
4
learning
4
test reliability
4
validity normative
4

Similar Publications

Background: Established risk models may not be applicable to patients at higher cardiovascular risk with a measured Lp(a) (lipoprotein[a]) level, a causal risk factor for atherosclerotic cardiovascular disease.

Methods: This was a model development study. The data source was the Nashville Biosciences Lp(a) data set, which includes clinical data from the Vanderbilt University Health System.

View Article and Find Full Text PDF

CardiacField: computational echocardiography for automated heart function estimation using two-dimensional echocardiography probes.

Eur Heart J Digit Health

January 2025

Department of Cardiovascular Surgery of Zhongshan Hospital, Fudan University, Shanghai 200032, China.

Aims: Accurate heart function estimation is vital for detecting and monitoring cardiovascular diseases. While two-dimensional echocardiography (2DE) is widely accessible and used, it requires specialized training, is prone to inter-observer variability, and lacks comprehensive three-dimensional (3D) information. We introduce CardiacField, a computational echocardiography system using a 2DE probe for precise, automated left ventricular (LV) and right ventricular (RV) ejection fraction (EF) estimations, which is especially easy to use for non-cardiovascular healthcare practitioners.

View Article and Find Full Text PDF

Use of artificial intelligence to predict outcomes in mild aortic valve stenosis.

Eur Heart J Digit Health

January 2025

Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA.

Aims: Aortic stenosis (AS) is a common and progressive disease, which, if left untreated, results in increased morbidity and mortality. Monitoring and follow-up care can be challenging due to significant variability in disease progression. This study aimed to develop machine learning models to predict the risks of disease progression and mortality in patients with mild AS.

View Article and Find Full Text PDF

Objective: To evaluate the clinical utility of improved machine learning models in predicting poor prognosis following endovascular intervention for intracranial aneurysms and to develop a corresponding visualization system.

Methods: A total of 303 patients with intracranial aneurysms treated with endovascular intervention at four hospitals (FuShun County Zigong City People's Hospital, Nanchong Central Hospital, The Third People's Hospital of Yibin, The Sixth People's Hospital of Yibin) from January 2022 to September 2023 were selected. These patients were divided into a good prognosis group ( = 207) and a poor prognosis group ( = 96).

View Article and Find Full Text PDF

Introduction: Case-based learning (CBL) utilizes authentic clinical cases that connect theory to practice. CBL has been shown to result in deeper learning and high engagement of adult learners. An open-source, web-based CBL module was created to help learners develop the cognitive foundation of ectopic pregnancy management in the low-resource setting.

View Article and Find Full Text PDF

Want 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!