Background: The STarT Back Screening Tool (SBST) was developed to stratify low back pain patients according to their risk of future physical disability so that prognostic subgroups can receive matched treatments in primary care.
Objective: To measure the construct and discriminative validity of the SBST-Brazil questionnaire.
Method: A hundred and fifty one patients were recruited to test the construct and discriminative validity comparing the SBST-Brazil to the Brazilian Version of the Oswestry Disability Index (ODI), Roland Morris Disability Questionnaire (RMDQ) and Fear-Avoidance Beliefs Questionnaire-Work (FABQ-W) and Physical Activity (FABQ-PA) subscales at baseline. Spearman's rank-order correlation and area under the curve (AUC) derived from receiver operating curves (ROC) for total scores and psychosocial subscale score of the SBST-Brazil were used for construct and discriminant validity analysis, respectively.
Results: The SBST-Brazil total and psychosocial subscale scores had good and moderate correlation with ODI (r=0.61; r=0.56, respectively) and good with RMDQ (r=0.70; r=0.64, respectively). Both scores of the SBST-Brazil total and psychosocial subscale correlated weakly and moderately with the FABQ-PA (r=0.28; r=0.34, respectively) and weakly with the FABQ-W (r=0.18; r=0.20, respectively). The discriminant validity with AUCs for the total and psychosocial subscale scores against reference standard ranged from 0.66 for kinesiophobia to 0.88 for disability.
Conclusion: The SBST-Brazil showed a moderate to good correlation with the disability tools, but a weak correlation with fear-avoidance beliefs. The results of discriminant validity suggest that SBST-Brazil is able to discriminate low back pain patients with disability and fear-avoidance beliefs.
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http://dx.doi.org/10.1016/j.bjpt.2016.12.006 | DOI Listing |
Digit Biomark
December 2024
Electrical and Computer Engineering, Western Michigan University, Kalamazoo, MI, USA.
Introduction: This research is focused on early detection of Alzheimer's disease (AD) using a multiscale feature fusion framework, combining biomarkers from memory, vision, and speech regions extracted from magnetic resonance imaging and positron emission tomography images.
Methods: Using 2D gray level co-occurrence matrix (2D-GLCM) texture features, volume, standardized uptake value ratios (SUVR), and obesity from different neuroimaging modalities, the study applies various classifiers, demonstrating a feature importance analysis in each region of interest. The research employs four classifiers, namely linear support vector machine, linear discriminant analysis, logistic regression (LR), and logistic regression with stochastic gradient descent (LRSGD) classifiers, to determine feature importance, leading to subsequent validation using a probabilistic neural network classifier.
ERJ Open Res
January 2025
Department of Smoking and COPD Research, National Institute of Respiratory Diseases, Mexico City, Mexico.
Background: COPD ranks as the third leading global cause of mortality. Despite the widespread use of the BODE index and its variants for mortality prediction, their accuracy may be affected by factors like ethnicity, altitude and regional disparities. This study aimed to assess a new altitude-adapted prognostic index in COPD patients at moderate altitudes compared with the BODE and other mortality predictors.
View Article and Find Full Text PDFInfect Drug Resist
January 2025
Department of Critical Care Medicine, The Fifth Clinical Medical College of Henan University of Chinese Medicine, Zhengzhou, Henan Province, People's Republic of China.
Background: Several predictive models for invasive pulmonary aspergillosis (IPA) based on clinical characteristics have been reported. Nevertheless, the significance of other concurrently detected microorganisms in IPA patients is equally noteworthy. This study aimed to develop a risk prediction model for IPA by integrating clinical and microbiological characteristics.
View Article and Find Full Text PDFFront Public Health
January 2025
Department of Vascular Surgery, Xuanwu Hospital, Capital Medical University, Beijing, China.
Background And Aim: Changes in cognitive function are commonly associated with aging in patients with cardiovascular diseases. The objective of this research was to construct and validate a nomogram-based predictive model for the identification of cognitive impairment in older people suffering from cardiovascular diseases.
Methods And Results: This retrospective study included 498 participants with cardiovascular diseases aged >60 selected from the NHANES 2011-2014.
Front Oncol
January 2025
School of Nursing, Chengdu Medical College, Chengdu, China.
Objective: Presentation delay of cancer patients prevents the patient from timely diagnosis and treatment leading to poor prognosis. Predicting the risk of presentation delay is crucial to improve the treatment outcomes. This study aimed to develop and validate prediction models of presentation delay risk in gastric cancer patients by using various machine learning models.
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