Factor loadings of the 6 domains with two-factor structure using exploratory factor analysis.

<p>Factor loadings of the 6 domains with two-factor structure using exploratory factor analysis.</p

Fit indices of the 10 domains of the ADLRS-III (n = 304).

<p>CFI = comparative fit index; TLI = Tucker-Lewis index; RMSEA = root mean square error of approximation; CI = confidence interval.</p><p>Fit indices of the 10 domains of the ADLRS-III (n = 304).</p

sj-doc-1-mdm-10.1177_0272989X211024980 – Supplemental material for Relationships among Antecedents, Processes, and Outcomes for Shared Decision Making: A Cross-Sectional Survey of Patients with Lumbar Degenerative Disease

Supplemental material, sj-doc-1-mdm-10.1177_0272989X211024980 for Relationships among Antecedents, Processes, and Outcomes for Shared Decision Making: A Cross-Sectional Survey of Patients with Lumbar Degenerative Disease by Chia-Hsien Chen, Hsin-Yi Chuang, Yen Lee, Glyn Elwyn, Wen-Hsuan Hou and Ken N. Kuo in Medical Decision Making</p

Construct Validity of the Chinese Version of the Activities of Daily Living Rating Scale III in Patients with Schizophrenia

<div><p>Background</p><p>The Chinese version of the Activities of Daily Living Rating Scale III (ADLRS-III), which has 10 domains, is commonly used for assessing activities of daily living (ADL) in patients with schizophrenia. However, construct validity (i.e., unidimensionality) for each domain of the ADLRS-III is unknown, limiting the explanations of the test results.</p><p>Purpose</p><p>This main purpose of this study was to examine unidimensionality of each domain in the ADLRS-III. We also examined internal consistency and ceiling/floor effects in patients with schizophrenia.</p><p>Methods</p><p>From occupational therapy records, we obtained 304 self-report data of the ADLRS-III. Confirmatory factor analysis (CFA) was conducted to examine the 10 one-factor structures. If a domain showed an insufficient model fit, exploratory factor analysis (EFA) was performed to investigate the factor structure and choose one factor representing the original construct. Internal consistency was examined using Cronbach’s alpha (α). Ceiling and floor effects were determined by the percentage of patients with the maximum and minimum scores in each domain, respectively.</p><p>Results</p><p>CFA analyses showed that 4 domains (i.e., leisure, picture recognition, literacy ability, communication tools use) had sufficient model fits. These 4 domains had acceptable internal consistency (α = 0.79-0.87) and no ceiling/floor effects, except the leisure domain which had a ceiling effect. The other 6 domains showed insufficient model fits. The EFA results showed that these 6 domains were two-factor structures.</p><p>Conclusion</p><p>The results supported unidimensional constructs of the leisure, picture recognition, literacy ability, and communication tool uses domains. The sum scores of these 4 domains can be used to represent their respective domain-specific functions. Regarding the 6 domains with insufficient model fits, we have explained the two factors of each domain and chosen one factor to represent its original construct. Future users may use the items from the chosen factors to assess domain-specific functions in patients with schizophrenia.</p></div

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20 juin 19171917/06/20 (A46).Appartient à l’ensemble documentaire : PoitouCh

Additional file 1 of A radiomics-based deep learning approach to predict progression free-survival after tyrosine kinase inhibitor therapy in non-small cell lung cancer

Additional file 1: Table S1. The formulae for the calculation of primary radiomic features. Table S2. Grid search results of DeepSurv hyper-parameters. Table S3. Comparisons of clinical characteristics between training and test sets. Table S4. Characteristics of clinical laboratory test. Table S5. Identified features for the model training in each DeepSurv model. Figure S1. The architecture of applied DeepSurv model. Figure S2. Schematic diagram of predictive risk-of-progression period in DeepSurv model