Quantifying Coloring Skills Among Preschoolers.

IMPORTANCE: Coloring is popular with preschool children and reveals their developmental state. However, interpreting coloring performances is challenging because descriptive and subjective evaluations are commonly used with large variations. OBJECTIVE: To develop a scoring method to objectively quantify children's coloring skills. DESIGN: Colored blank train templates were analyzed using four indicators (entropy, complexity, coloring outside the lines, and unexpected blank areas) to form a summed score. SETTING: Kindergarten in a urban city (Tainan, Taiwan). PARTICIPANTS: Two hundred thirty-nine typically developing children ages 3 to 6 yr. OUTCOME AND MEASURES: A newly developed method to assess coloring skill on the basis of a colored picture of a train. RESULTS: The summed score exhibited good internal consistency (Cronbach's α = .80), discriminative validity (p = .04), convergent validity (rs = .66 and .59 with age and visual-motor integration), and acceptable factorial validity (comparative fit index = .99, standardized root-mean-square residual = .04, and root-mean-square error of approximation = .13). Moreover, three coloring patterns (mature, transitional, and immature) were identified. CONCLUSIONS AND RELEVANCE: The new method provides objective, reliable, and valid scores representing coloring skills in typically developing children. In addition, the coloring patterns can be recognized. This method can be used to facilitate comparisons of children's coloring skills with peers and provide valuable insight into children's development. Plain-Language Summary: This study proposes a new method to objectively quantify children's coloring skills with sound reliability and validity in typically developing children. The method can be used to evaluate children's coloring skills and patterns to shed light on their developmental stages.

Patient-Reported Variables Affecting Self-Perceived Overall Recovery Among People With Subacute Stroke.

IMPORTANCE: Patients' perception of overall recovery is a critical outcome for stroke rehabilitation. However, the perception of overall recovery cannot be obtained using multidimensional measures, because satisfaction in most domains of life does not guarantee satisfaction in overall recovery. A single overall recovery score seems a straightforward measure. However, the clinical implications of overall scores are restricted, because factors affecting patients' overall recovery are unclear, which can be prioritized to optimize the effectiveness of rehabilitation. OBJECTIVE: To examine patient-reported variables affecting overall recovery scores in patients with differing stroke severity. DESIGN: The 59 items of the Stroke Impact Scale 3.0 were selected using regression analysis with a forward selection to explain the overall recovery score (0% = no recovery; 100% = full recovery). Stroke severity was determined with the National Institutes of Health Stroke Scale. SETTING: Hospitals. PARTICIPANTS: Data of 950 patients collected 90 days after stroke. RESULTS: The models explained about 55% of the variance of the overall recovery scores with five to nine variables, but merely 16% of the variance was explained for patients with moderate stroke. As stroke severity increased, the number of identified variables decreased. Most identified variables were related to social participation and self-care activities (e.g., ability to help others, control the bowels, and dress the torso). Differences in the remaining variables depended on stroke severity. CONCLUSIONS AND RELEVANCE: Patients' priorities differ depending on stroke severity. The identified variables may be set as treatment goals to optimize patients' self-perceived overall recovery. Plain-Language Summary: How patients perceive their overall recovery after a stroke is a critical outcome for their stroke rehabilitation. This study demonstrated that patients with different stroke severity may have different priorities that influence their self-perceived levels of overall recovery. The variables identified in this study may help occupational therapy practitioners identify meaningful goals to optimize patients' self-perceived overall recovery.

Validation of the Machine Learning-Based Stroke Impact Scale With a Cross-Cultural Sample.

IMPORTANCE: The machine learning-based Stroke Impact Scale (ML-SIS) is an efficient short-form measure that uses 28 items to provide domain scores comparable to those of the original 59-item Stroke Impact Scale-Third Edition (SIS 3.0). However, its utility is largely unknown because it has not been cross-validated with an independent sample. OBJECTIVE: To examine the ML-SIS's comparability and test-retest reliability with that of the original SIS 3.0 in an independent sample of people with stroke. DESIGN: Comparability was examined with the coefficient of determination (R2), mean absolute error, and root-mean-square error (RMSE). Test-retest reliability was examined using the intraclass correlation coefficient (ICC). SETTING: Five hospitals in Taiwan. PARTICIPANTS: Data of 263 persons with stroke were extracted from a previous study; 144 completed repeated assessments after a 2-wk interval. RESULTS: High R2 (.87-.95) and low mean absolute error or RMSE (about 2.4 and 3.3) of the domain scores, except for the Emotion scores (R2 = .08), supported the comparability of the two measures. Similar ICC values (.39-.87 vs. .46-.87) were found between the two measures, suggesting that the ML-SIS is as reliable as the SIS 3.0. CONCLUSIONS AND RELEVANCE: The ML-SIS provides scores mostly identical to those of the original measure, with similar test-retest reliability, except for the Emotion domain. Thus, it is a promising alternative that can be used to lessen the burden of routine assessments and provide scores comparable to those of the original SIS 3.0. Plain-Language Summary: The machine learning-based Stroke Impact Scale (ML-SIS) is as reliable as the original Stroke Impact Scale-Third Edition, except for the Emotion domain. Thus, the ML-SIS can be used to improve the efficiency of clinical assessments and also relieve the burden on people with stroke who are completing the assessments.

Developing a machine learning-based short form of the positive and negative syndrome scale.

BACKGROUND AND HYPOTHESIS: The Positive and Negative Syndrome Scale (PANSS) consists of 30 items and takes up to 50 minutes to administer and score. Therefore, this study aimed to develop and validate a machine learning-based short form of the PANSS (PANSS-MLSF) that reproduces the PANSS scores. Moreover, the PANSS-MLSF estimated the removed-item scores. STUDY DESIGN: The PANSS-MLSF was developed using an artificial neural network, and the removed-item scores were estimated using the eXtreme Gradient Boosting classifier algorithm. The reliability of the PANSS-MLSF was examined using Cronbach's alpha. The concurrent validity was examined by the association (Pearson's r) between the PANSS-MLSF and the PANSS. The convergent validity was examined by the association (Pearson's r) between the PANSS-MLSF and the Clinical Global Impression-Severity, Mini-Mental State Examination, and Lawton Instrumental Activities of Daily Living Scale. The agreement of the estimated removed-item scores with their original scores was examined using Cohen's kappa. STUDY RESULTS: Our analysis included data from 573 patients with moderate severity. The two versions of the PANSS-MLSF comprised 15 items and 9 items were proposed. The PANSS-MLSF scores were similar to the PANSS scores (mean squared error=2.6-24.4 points). The reliability, concurrent validity, and convergent validity of the PANSS-MLSF were good. Moderate to good agreement between the estimated removed-item scores and the original item scores was found in 60% of the removed items. CONCLUSION: The PANSS-MLSF offers a viable way to reduce PANSS administration time, maintain score comparability, uphold reliability and validity, and even estimate scores for the removed items.

Developing an Accumulative Assessment System of Upper Extremity Motor Function in Patients With Stroke Using Deep Learning.

OBJECTIVE: The Fugl-Meyer assessment for upper extremity (FMA-UE) is a measure for assessing upper extremity motor function in patients with stroke. However, the considerable administration time of the assessment decreases its feasibility. This study aimed to develop an accumulative assessment system of upper extremity motor function (AAS-UE) based on the FMA-UE to improve administrative efficiency while retaining sufficient psychometric properties. METHODS: The study used secondary data from 3 previous studies having FMA-UE datasets, including 2 follow-up studies for subacute stroke individuals and 1 test-retest study for individuals with chronic stroke. The AAS-UE adopted deep learning algorithms to use patients' prior information (ie, the FMA-UE scores in previous assessments, time interval of adjacent assessments, and chronicity of stroke) to select a short and personalized item set for the following assessment items and reproduce their FMA-UE scores. RESULTS: Our data included a total of 682 patients after stroke. The AAS-UE administered 10 different items for each patient. The AAS-UE demonstrated good concurrent validity (r = 0.97-0.99 with the FMA-UE), high test-retest reliability (intra-class correlation coefficient = 0.96), low random measurement error (percentage of minimal detectable change = 15.6%), good group-level responsiveness (standardized response mean = 0.65-1.07), and good individual-level responsiveness (30.5%-53.2% of patients showed significant improvement). These psychometric properties were comparable to those of the FMA-UE. CONCLUSION: The AAS-UE uses an innovative assessment method, which makes good use of patients' prior information to achieve administrative efficiency with good psychometric properties. IMPACT: This study demonstrates a new assessment method to improve administrative efficiency while retaining psychometric properties, especially individual-level responsiveness and random measurement error, by making good use of patients' basic information and medical records.

Enhancing Cordycepin in Functional Porridge through Rice Varietal Fermentation with Cordyceps militaris (Ascomycetes) and Utilizing Non-Targeted Metabolomics for Process Enhancement.

Cordyceps militaris, a medicinal fungus rich in cordycepin, shows promise in treating diseases such as cancer, respiratory issues, and COVID-19. This study examines the impact of different Taiwanese rice varieties on its solid-state fermentation, focusing on optimizing cordycepin production. The results indicated that the cordycepin yield was indeed affected by the type of rice used. In terms of the fruiting bodies, germ rice resulted in the highest yield (13.1 ± 0.36 mg/g), followed by brown rice (11.9 ± 0.26 mg/g). In the rice culture medium (RCM), brown rice led to the highest yield (4.77 ± 0.06 mg/g). Using gas chromatography-mass spectrometry and untargeted metabolomics, the study identifies four key volatile components linked to cordycepin, providing insights into developing functional rice porridge products. These findings are significant for advancing cordycepin mass production and offering dietary options for older individuals.

Factors Contributing to Non-Concordance Between End-of-Life Care and Advance Care Planning.

CONTEXT: Despite making do-not-resuscitate or comfort care decisions during advance care planning, terminally ill patients sometimes receive life-sustaining treatments as they approach end of life. OBJECTIVES: To examine factors contributing to nonconcordance between end-of-life care and advance care planning. METHODS: In this longitudinal retrospective cohort study, terminally ill patients with a life expectancy shorter than six months, who had previously expressed a preference for do-not-resuscitate or comfort care, were followed up after palliative shared care intervention. An instrument with eight items contributing to non-concordant care, developed through literature review and experts' consensus, was employed. An expert panel reviewed electronic medical records to determine factors associated with non-concordant care for each patient. Statistical analysis, including descriptive statistics and the chi-square test, examines demographic characteristics, and associations. RESULTS: Among the enrolled 7871 patients, 97 (1.2%) received non-concordant care. The most prevalent factor was "families being too distressed about the patient's deteriorating condition and therefore being unable to let go" (84.5%) followed by "limited understanding of medical interventions among patients and surrogates" (38.1%), and "lack of patient participation in the decision-making process" (25.8%). CONCLUSIONS: This study reveals that factors related to relational autonomy, emotional support, and health literacy may contribute to non-concordance between advance care planning and end-of-life care. In the future, developing an advance care planning model emphasizes respecting relational autonomy, providing emotional support, and enhancing health literacy could help patients receiving a goal concordant and holistic end-of-life care.

Development of a Rasch-calibrated emotion recognition video test for patients with schizophrenia.

Patients with schizophrenia tend to have deficits in emotion recognition (ER) that affect their social function. However, the commonly-used ER measures appear incomprehensive, unreliable and invalid, making it difficult to comprehensively evaluate ER. The purposes of this study were to develop the Computerized Emotion Recognition Video Test (CERVT) evaluating ER ability in patients with schizophrenia. This study was divided into two phases. First, we selected candidate CERVT items/videos of 8 basic emotion domains from a published database. Second, we validated the selected CERVT items using Rasch analysis. Finally, the 269 patients and 177 healthy adults were recruited to ensure the participants had diverse abilities. After the removal of 21 misfit (infit or outfit mean square > 1.4) items and adjustment of the item difficulties of the 26 items with severe differential item functioning, the remaining 217 items were finalized as the CERVT items. All the CERVT items showed good model fits with small eigenvalues (≤ 2) based on the residual-based principal components analysis for each domain, supporting the unidimensionality of these items. The 8 domains of the CERVT had good to excellent reliabilities (average Rasch reliabilities = 0.84-0.93). The CERVT contains items of the 8 basic emotions with individualized scores. Moreover, the CERVT showed acceptable reliability and validity, and the scores were not affected by examinees' gender. Thus, the CERVT has the potential to provide a comprehensive, reliable, valid, and gender-unbiased assessment of ER for patients with schizophrenia.