ReviewJournal of stroke2022
Predictors of Therapy Response in Chronic Aphasia: Building a Foundation for Personalized Aphasia Therapy.
Review in Journal of stroke, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled 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.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
29 citing papers in PubMed, 1 synthesis or guideline pooled it, 42 citations in OpenAlex.
- A Meta-Analysis of Anomia Treatment in Bilingual Aphasia: Within- and Cross-Language Generalization and Predictors of the Treatment Outcomes.Journal of speech, language, and hearing research : JSLHR · 2024Pooled it
- Microstructural properties in subacute aphasia: concurrent and prospective relationships underpinning recovery.Brain structure & function · 2024Trial
- White Matter Hyperintensities as a Predictor of Aphasia Recovery.Archives of physical medicine and rehabilitation · 2024Trial
- Predicting Outcomes of Language Rehabilitation: Prognostic Factors for Immediate and Long-Term Outcomes After Aphasia Therapy.Journal of speech, language, and hearing research : JSLHR · 2023Trial
- White matter predictors of cerebellar tDCS treatment effects in aphasia rehabilitation.Frontiers in neurologyTrial
- Beyond the Plateau: Repeated Treatment Opportunities Are Associated with Improved Naming in Chronic Post-Stroke Aphasia.Brain sciences · 2026Article
- Right hemisphere involvement in intensive aphasia therapy outcomes: a multimodal neuroimaging concept study.Journal of neurology · 2026Article
- Article
- Narrative Discourse Predictors of Response to Naming Intervention in Aphasia.Journal of speech, language, and hearing research : JSLHR · 2026Article
- Acute-Phase Machine Learning Prediction of 12-Month Aphasia and Discourse Recovery.medRxiv : the preprint server for health sciences · 2026Article
- Hemispheric Network Dynamics During Auditory Language Comprehension and Its Clinical Implications Regarding Resting-State Functional Magnetic Resonance Imaging.Journal of speech, language, and hearing research : JSLHR · 2026Article
- Aphasia Rehabilitation in India: Current Practices and Future Directions.Healthcare (Basel, Switzerland) · 2026Article
- Predicting Post-Stroke Aphasia Speech Performance from Multimodal Data with Explainable Machine Learning.bioRxiv : the preprint server for biology · 2026Article
- Beyond the cerebral cortex: cerebellar language-related subregions contributions to fluency in post-stroke aphasia.NeuroImage. Clinical · 2026Article
- Knowledge, Attitudes, and Practices Toward Speech and Language Rehabilitation Among Family Members of Patients with Post-Stroke Aphasia in Inner Mongolia.Journal of multidisciplinary healthcare · 2026Article
- Development and Psychometric Validation of the Cyprus Aphasia Screening Test (CAST).Brain sciences · 2025Article
- Naming practice effects and inconsistencies relate to treatment outcome in people with aphasia.Neuropsychologia · 2025Article
- Cognitive Functions Supporting Learning Over Time in Naming Treatment for Aphasia.Journal of speech, language, and hearing research : JSLHR · 2025Article
- Multifactorial interplay on language recovery following left-hemispheric stroke: A retrospective study.Journal of neuropsychology · 2025Article
- An Introduction to Machine Learning for Speech-Language Pathologists: Concepts, Terminology, and Emerging Applications.Perspectives of the ASHA special interest groups · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Chronic aphasia, a devastating impairment of language, affects up to a third of stroke survivors. Speech and language therapy has consistently been shown to improve language function in prior clinical trials, but few clinicially applicable predictors of individual therapy response have been identified to date. Consequently, clinicians struggle substantially with prognostication in the clinical management of aphasia. A rising prevalence of aphasia, in particular in younger populations, has emphasized the increasing demand for a personalized approach to aphasia therapy, that is, therapy aimed at maximizing language recovery of each individual with reference to evidence-based clinical recommendations. In this narrative review, we discuss the current state of the literature with respect to commonly studied predictors of therapy response in aphasia. In particular, we focus our discussion on biographical, neuropsychological, and neurobiological predictors, and emphasize limitations of the literature, summarize consistent findings, and consider how the research field can better support the development of personalized aphasia therapy. In conclusion, a review of the literature indicates that future research efforts should aim to recruit larger samples of people with aphasia, including by establishing multisite aphasia research centers.
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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.