Machine Learning Predictions of Recovery in Bilingual Poststroke Aphasia: Aligning Insights With Clinical Evidence.

Background: Predicting treated language improvement (TLI) and transfer to the untreated language (cross-language generalization, CLG) after speech-language therapy in bilingual individuals with poststroke aphasia is crucial for personalized treatment planning. This study evaluated machine learning models to predict TLI and CLG and identified the key predictive features (eg, patient severity, demographics, and treatment variables) aligning with clinical evidence.

Methods: Forty-eight Spanish-English bilingual individuals with poststroke aphasia received 20 sessions of semantic feature-based naming treatment in either their first or second language.

The cognate facilitation effect on lexical access in bilingual aphasia: Evidence from the Boston Naming Test.

Most cognate research suggests facilitation effects in picture naming, but how these effects manifest in bilinguals after brain damage remains unclear. Additionally, whether this effect is captured in clinical measures is largely unknown. Using data from the Boston Naming Test, we examined the naming of cognates and noncognates, the extent of cognate facilitation produced, and the individual differences in bilingual language experience associated with naming outcomes in forty Spanish-English bilingual persons with aphasia (BPWA) relative to thirty-one Spanish-English healthy bilinguals (HB).

Association Between Social Determinants of Health and Communication Difficulties in Poststroke U.S. Hispanic and Non-Hispanic White Populations.

Purpose: The relationship among ethnicity, social determinants of health (SDOH), and disparities in poststroke outcomes is complex, and the impact on communication difficulties is unclear. This study investigated the presence and nature of communication difficulties in poststroke non-Hispanic White (PsnHw) and Hispanic U.S.