ArticleBMJ open2020
Predicting treatment outcomes for bilinguals with aphasia using computational modeling: Study protocol for the PROCoM randomised controlled trial.
Article in BMJ open, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT02916524 (Double-blind Randomized Trial Investigating the Use of Computational Modeling to Predict Rehabilitation Outcomes in Bilingual Aphasia), which is not on this map. Cited by 18 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Double-blind Randomized Trial Investigating the Use of Computational Modeling to Predict Rehabilitation Outcomes in Bilingual Aphasia
Who cites it
18 citing papers in PubMed, 25 citations in OpenAlex.
- Measurement of cross-language and cross-domain generalization following semantic feature-based anomia treatment in bilingual aphasia.Neuropsychological rehabilitation · 2026Trial
- Computational Models for Bilingual Aphasia: A Systematic Review of Language Deficit Research with a Focus on Code-Switching and Translation.Behavioral sciences (Basel, Switzerland) · 2026Review
- Predicting bilingual aphasia treatment outcomes using digital twins: a double-blind randomized controlled trial.NPJ digital medicine · 2026Article
- Evidence for an integrated bilingual language system from discourse tasks in aphasia.Aphasiology · 2026Article
- Evidence for an Integrated Bilingual Language System from Discourse Tasks in Aphasia.bioRxiv : the preprint server for biology · 2025Article
- Navigating the Complexity of Bilingual Aphasia: Current Insights and Future Directions.Brain sciences · 2025Review
- The evolution of word retrieval errors during semantic feature-based therapy in bilingual aphasia.Bilingualism (Cambridge, England) · 2025Article
- Cross-language interaction during sequential anomia treatment in three languages: Evidence from a trilingual person with aphasia.Cortex; a journal devoted to the study of the nervous system and behavior · 2025Article
- Machine Learning Predictions of Recovery in Bilingual Poststroke Aphasia: Aligning Insights With Clinical Evidence.Stroke · 2025Article
- Charting the Course of Aphasia Recovery: Factors, Trajectories, and Outcomes.Annual review of linguistics · 2025Article
- Multilevel factors predict treatment response following semantic feature-based intervention in bilingual aphasia.Bilingualism (Cambridge, England) · 2024Article
- AI-assisted assessment and treatment of aphasia: a review.Frontiers in public health · 2024Review
- Understanding, facilitating and predicting aphasia recovery after rehabilitation.International journal of speech-language pathology · 2022Review
- LEX-BADAT: Language EXperience in Bilinguals With and Without Aphasia DATaset.Frontiers in psychology · 2022Article
- Aphasia in Multilingual Patients.Current neurology and neuroscience reports · 2021Review
- Predicting language treatment response in bilingual aphasia using neural network-based patient models.Scientific reports · 2021Article
- Telerehabilitation for Word Retrieval Deficits in Bilinguals With Aphasia: Effectiveness and Reliability as Compared to In-person Language Therapy.Frontiers in neurology · 2021Article
- Clinical perspectives and strategies for confronting disparities in social determinants of health for Hispanic bilinguals with aphasia.Journal of communication disordersArticle
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
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Abstract
introductionBilinguals with aphasia (BWA) present varying degrees of lexical access impairment and recovery across their two languages. Because both languages may benefit from therapy, identifying the optimal target language for treatment is a current challenge for research and clinical practice. Prior research has demonstrated that the BiLex computational model can accurately simulate lexical access in healthy bilinguals, and language impairment and treatment response in bilingual aphasia. Here, we aim to determine whether BiLex can predict treatment outcomes in BWA in the treated and the untreated language and compare these outcome predictions to determine the optimal language for rehabilitation. METHODS AND ANALYSIS: The study involves a prospective parallel-group, double-blind, randomised controlled trial. Forty-eight Spanish-English BWA will receive 20 sessions of semantic treatment for lexical retrieval deficits in one of their languages and will complete assessments in both languages prior and after treatment. Participants will be randomly assigned to an experimental group receiving treatment in the optimal language determined by the model or a control group receiving treatment in the language opposite to the model's recommendation. Primary treatment outcomes include naming probes while secondary treatment outcomes include tests tapping additional language domains. Treatment outcomes will be compared across the two groups using 2×2 mixed effect models for repeated measures Analysis of variance (ANOVA) on metrics of treatment effects commonly employed in rehabilitation studies (ie, effect size and percentage change). ETHICS AND DISSEMINATION: All procedures included in this protocol (protocol number 29, issue date: 19 March 2019) were approved by the Boston University Charles River Campus Institutional Review Board at Boston, Massachusetts (reference number: 4492E). The results of this study will be published in peer-reviewed scientific journals and will be presented at national and international conferences. TRIAL REGISTRATION NUMBER: NCT02916524.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.