Evidence map›Paper›PMID 41963587›Full record

ArticleNPJ digital medicine2026

Predicting bilingual aphasia treatment outcomes using digital twins: a double-blind randomized controlled trial.

Swathi Kiran, Erin Carpenter, Uli Grasemann, Michael Scimeca, Manuel J Marte, Marissa Russell-Meill, Claudia Peñaloza, Yorghos Tripodis, Risto Miikkulainen

Registry-linked trialAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NPJ digital medicine, 2026. 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 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

NCT02916524 naunknown statusnot on this map

Double-blind Randomized Trial Investigating the Use of Computational Modeling to Predict Rehabilitation Outcomes in Bilingual Aphasia

TypeinterventionalSponsorBoston University Charles River CampusRan2018 to 2023Enrolled48ConditionsAphasiaArmsSemantic Feature Analysis (SFA)
3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Acute-Phase Machine Learning Prediction of 12-Month Aphasia and Discourse Recovery.medRxiv : the preprint server for health sciences · 2026
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Swathi KiranDepartment of Speech, Language & Hearing Sciences, Center for Brain Recovery, Boston University, Boston, MA, USA. kirans@bu.edu.
Erin CarpenterDepartment of Speech, Language & Hearing Sciences, Center for Brain Recovery, Boston University, Boston, MA, USA.
Uli GrasemannDepartment of Computer Sciences, University of Texas at Austin, Austin, TX, USA.
Michael ScimecaDepartment of Speech, Language & Hearing Sciences, Center for Brain Recovery, Boston University, Boston, MA, USA.
Manuel J MarteDepartment of Speech, Language & Hearing Sciences, Center for Brain Recovery, Boston University, Boston, MA, USA.
Marissa Russell-MeillDepartment of Speech, Language & Hearing Sciences, Center for Brain Recovery, Boston University, Boston, MA, USA.
Claudia PeñalozaDepartment of Cognition, Development and Educational Psychology, Faculty of Psychology, University of Barcelona, Barcelona, Spain.
Yorghos TripodisDepartment of Biostatistics, School of Public Health, Boston University, Boston, MA, USA.
Risto MiikkulainenDepartment of Computer Sciences, University of Texas at Austin, Austin, TX, USA.

Funding

NIH HHS DC014922
6 · The paper itself

Abstract

Bilingual aphasia rehabilitation faces the challenge of determining which language to target in therapy to maximize recovery across both languages. This double-blind randomized controlled trial (48 Spanish-English bilinguals with chronic aphasia; NCT02916524) evaluated whether the BiLex computational model could predict the optimal language for aphasia therapy. Participants received 40 h of semantic feature-based treatment in either the BiLex-recommended language or the opposite language. Both groups showed similar gains in treated-language naming, with no significant difference in proportion of maximal improvement (Difference (SE) = -0.03 (0.07); t = -0.46; p = 0.65). However, the model-opposite group showed significantly greater cross-language generalization (Difference (SE) = -0.16 (0.07); t = -2.38; p = 0.02), though with higher response variability. Further, when the participants were divided into subgroups according to performance, the model-assigned group had a significant advantage in all but the lowest performing subgroups. All these differences were consistent with BiLex model predictions.

Identifiers

PMID41963587
PMCPMC13265940

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.