English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

PURPOSE: To create a model to predict independence in the activities of daily living at discharge in stroke patients in the convalescence stage. The study also examined whether the predictability of functional independence at discharge would be improved by creating a specific prediction model for each rehabilitation facility. METHODS: To create the prediction model, data of 65 first stroke patients were analyzed using stepwise multiple regression analysis. Age, time post-stroke, Functional Independence Measure motor subscale score, Functional Independence Measure cognitive subscale score, Stroke Impairment Assessment Set, Berg Balance Scale, and Vitality Index at admission were selected as predictor variables of Functional Independence Measure motor subscale score at discharge. The accuracy of this model was compared with an existing prognosis model using data from 98 first-stroke patients, comparing the difference between actual and predicted Functional Independence Measure motor subscale score at discharge for each model. RESULTS: The prediction formula created included admission Functional Independence Measure motor subscale score, Vitality Index, age, and Stroke Impairment Assessment Set score. The adjusted R square value was 0.60. The prediction errors of the new and previous models were −2.5 ± 10.8 and −18.3 ± 18.7, respectively, which were significantly different. CONCLUSION: Our results suggest that prediction accuracy may be improved by creating prediction formulas specifically for each institution.

<b>A prediction model for activities of daily living for stroke patients in a convalescent rehabilitation ward</b><b> </b>