Development of a Berg Balance Scale Short-Form Using a Machine Learning Approach in Patients With Stroke.

BACKGROUND AND PURPOSE: The Berg Balance Scale (BBS) is frequently used in routine clinical care and research settings and has good psychometric properties. This study was conducted to develop a short form of the BBS using a machine learning approach (BBS-ML). METHODS: Data of 408 individuals poststroke were extracted from a published database. The initial (ie, 4-, 5-, 6-, 7-, and 8-item) versions were constructed by selecting top-ranked items based on the feature selection algorithm in the artificial neural network model. The final version of the BBS-ML was chosen by selecting the short form that used a smaller number of items to achieve a higher predictive power R2 , a lower 95% limit of agreement (LoA), and an adequate possible scoring point (PSP). An independent sample of 226 persons with stroke was used for external validation. RESULTS: The R2 values for the initial 4-, 5-, 6-, 7-, and 8-item short forms were 0.93, 0.95, 0.97, 0.97, and 0.97, respectively. The 95% LoAs were 14.2, 12.2, 9.7, 9.6, and 8.9, respectively. The PSPs were 25, 35, 34, 35, and 36, respectively. The 6-item version was selected as the final BBS-ML. Preliminary external validation supported its performance in an independent sample of persons with stroke ( R2 = 0.99, LoA = 10.6, PSP = 37). DISCUSSION AND CONCLUSIONS: The BBS-ML seems to be a promising short-form alternative to improve administrative efficiency. Future research is needed to examine the psychometric properties and clinical usage of the 6-item BBS-ML in various settings and samples.Video Abstract available for more insights from the authors (see the Video, Supplemental Digital Content 1, available at: http://links.lww.com/JNPT/A402 ).

Development of a Rasch-Calibrated Test for Assessing Implied Meaning in Patients With Schizophrenia.

IMPORTANCE: Patients with schizophrenia tend to have severe deficits in theory of mind, which may limit their interpretation of others' behaviors and thereby hamper social participation. Commonly used measures of theory of mind assess the ability to understand various social situations (e.g., implied meaning or hinting, faux pas), but these measures do not yield valid, reliable, and gender unbiased results to inform interventions for managing theory-of-mind deficits. We used understanding of implied meaning, which appears to be a unidimensional construct highly correlated with social competence, as a promising starting point to develop a theory-of-mind assessment. OBJECTIVE: To develop a Rasch-calibrated computerized test of implied meaning. DESIGN: Cross-sectional design. SETTING: Psychiatric hospitals and community. PARTICIPANTS: 344 participants (240 patients with schizophrenia and 104 healthy adults). RESULTS: We initially developed 27 items for the Computerized Implied Meaning Test. After inappropriate items (12 misfit items and 1 gender-biased item) were removed, the remaining 14 items showed acceptable model fit to the Rasch model (infit = 0.84-1.16; outfit = 0.65-1.34) and the one-factor model (comparative fit index = .91, standardized root mean square residual = .05, root-mean-square error of approximation = .08). Most patients (81.7%) achieved individual Rasch reliability of ≥.90. Healthy participants performed significantly better on the test than patients with schizophrenia (Cohen's d = 2.5, p < .001). CONCLUSIONS AND RELEVANCE: Our preliminary findings suggest that the Computerized Implied Meaning Test may provide reliable, valid, and gender-unbiased results for patients with schizophrenia. What This Article Adds: We developed a new measure for assessing theory-of-mind ability in patients with schizophrenia that consists of items targeting the understanding of implied meaning. Preliminary findings suggest that the Computerized Implied Meaning Test is reliable, valid, and gender unbiased and may be used in evaluating patients' theory-of-mind deficits and relevant factors.

Development of a Short-Form Stroke Impact Scale Using a Machine Learning Algorithm for Patients at the Subacute Stage.

IMPORTANCE: Several short forms of the Stroke Impact Scale Version 3.0 (SIS 3.0) have been proposed in order to decrease its administration time of about 20 min. However, none of the short-form scores are comparable to those of the original measure. OBJECTIVE: To develop a short-form SIS 3.0 using a machine learning algorithm (ML-SIS). DESIGN: We developed the ML-SIS in three stages. First, we calculated the frequencies of items having the highest contribution to predicting the original domain scores across 50 deep neural networks. Second, we iteratively selected the items showing the highest frequency until the coefficient of determination (R2) of each domain was ≥.90. Third, we examined the comparability and concurrent and convergent validity of the ML-SIS. SETTING: Hospitals. PARTICIPANTS: We extracted complete data for 1,010 patients from an existing data set. RESULTS: Twenty-eight items were selected for the ML-SIS. High average R2s (.90-.96) and small average residuals (mean absolute errors and root-mean-square errors = 0.49-2.84) indicate good comparability. High correlations (rs = .95-.98) between the eight domain scores of the ML-SIS and the SIS 3.0 indicate sufficient concurrent validity. Similar interdomain correlations between the two measures indicate satisfactory convergent validity. CONCLUSIONS AND RELEVANCE: The ML-SIS uses about half of the items in the SIS 3.0, has an estimated administration time of 10 min, and provides valid scores comparable to those of the original measure. Thus, the ML-SIS may be an efficient alternative to the SIS 3.0. What This Article Adds: The ML-SIS, a short form of the SIS 3.0 developed using a machine learning algorithm, shows good potential to be an efficient and informative measure for clinical settings, providing scores that are valid and comparable to those of the original measure.

Cyclin-dependent kinase 5 modulates STAT3 and androgen receptor activation through phosphorylation of Ser7²7 on STAT3 in prostate cancer cells.

Cyclin-dependent kinase 5 (Cdk5) is known to regulate prostate cancer metastasis. Our previous results indicated that Cdk5 activates androgen receptor (AR) and supports prostate cancer growth. We also found that STAT3 is a target of Cdk5 in promoting thyroid cancer cell growth, whereas STAT3 may play a role as a regulator to AR activation under cytokine control. In this study, we investigated the regulation of Cdk5 and its activator p35 on STAT3/AR signaling in prostate cancer cells. Our results show that Cdk5 biochemically interacts with STAT3 and that this interaction depends on Cdk5 activation in prostate cancer cells. The phosphorylation of STAT3 at Ser7²7 (p-Ser7²7-STAT3) is regulated by Cdk5 in cells and xenograft tumors. The mutant of STAT3 S727A reduces its interaction with Cdk5. We further show that the nuclear distribution of p-Ser7²7-STAT3 and the expression of STAT3-regulated genes (junB, c-fos, c-myc, and survivin) are regulated by Cdk5 activation. STAT3 mutant does not further decrease cell proliferation upon Cdk5 inhibition, which implies that the role of STAT3 regulated by Cdk5 correlates to cell proliferation control. Interestingly, Cdk5 may regulate the interaction between STAT3 and AR through phosphorylation of Ser7²7-STAT3 and therefore upregulate AR protein stability and transactivation. Correspondingly, clinical evidence shows that the level of p-Ser7²7-STAT3 is significantly correlated with Gleason score and the levels of upstream regulators (Cdk5 and p35) as well as downstream protein (AR). In conclusion, this study demonstrates that Cdk5 regulates STAT3 activation through Ser7²7 phosphorylation and further promotes AR activation by protein-protein interaction in prostate cancer cells.