Regis University

About: Regis University is a education organization based out in Denver, Colorado, United States. It is known for research contribution in the topics: Health care & Population. The organization has 1235 authors who have published 1540 publications receiving 24724 citations. The organization is also known as: Regis & Regis College.

A psychometric toolbox for testing validity and reliability.

Abstract: Purpose: To review the concepts of reliability and validity, provide examples of how the concepts have been used in nursing research, provide guidance for improving the psychometric soundness of instruments, and report suggestions from editors of nursing journals for incorporating psychometric data into manuscripts. Methods: CINAHL, MEDLINE, and PsycINFO databases were searched using key words: validity, reliability, and psychometrics. Nursing research articles were eligible for inclusion if they were published in the last 5 years, quantitative methods were used, and statistical evidence of psychometric properties were reported. Reports of strong psychometric properties of instruments were identified as well as those with little supporting evidence of psychometric soundness. Findings: Reports frequently indicated content validity but sometimes the studies had fewer than five experts for review. Criterion validity was rarely reported and errors in the measurement of the criterion were identified. Construct validity remains underreported. Most reports indicated internal consistency reliability (α) but few reports included reliability testing for stability. When retest reliability was asserted, time intervals and correlations were frequently not included. Conclusions: Planning for psychometric testing through design and reducing nonrandom error in measurement will add to the reliability and validity of instruments and increase the strength of study findings. Underreporting of validity might occur because of small sample size, poor design, or lack of resources. Lack of information on psychometric properties and misapplication of psychometric testing is common in the literature.

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Abstract: Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumoris a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses thestate-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross tota lresection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset.

Neck Pain: Clinical Practice Guidelines Linked to the International Classification of Functioning, Disability, and Health From the Orthopaedic Section of the American Physical Therapy Association

Abstract: The Orthopaedic Section of the American Physical Therapy Association presents this second set of clinical practice guidelines on neck pain, linked to the International Classification of Functioning, Disability, and Health (ICF). The purpose of these practice guidelines is to describe evidence-based orthopaedic physical therapy clinical practice and provide recommendations for (1) examination and diagnostic classification based on body functions and body structures, activity limitations, and participation restrictions, (2) prognosis, (3) interventions provided by physical therapists, and (4) assessment of outcome for common musculoskeletal disorders.