Evidence map›Paper›PMID 33208330›Full record

ArticleBMJ open2020

Predicting treatment outcomes for bilinguals with aphasia using computational modeling: Study protocol for the PROCoM randomised controlled trial.

Claudia Peñaloza, Maria Dekhtyar, Michael Scimeca, Erin Carpenter, Nishaat Mukadam, Swathi Kiran

Registry-linked trialOpen access · goldAbstract readClinical Trial Protocol
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed
1.5field-weighted citation impact, top 18% of its field
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

18 citing papers in PubMed, 25 citations in OpenAlex.

  1. Trial
  2. Review
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  11. Article
  12. Review
  13. Understanding, facilitating and predicting aphasia recovery after rehabilitation.International journal of speech-language pathology · 2022
    Review
  14. Article
  15. Aphasia in Multilingual Patients.Current neurology and neuroscience reports · 2021
    Review
  16. Article
  17. Article
  18. 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

6 authors at 1 institution in 1 country.

Claudia PeñalozaAphasia Research Laboratory, Department of Speech, Language and Hearing Sciences, Boston University, Boston, Massachusetts, USA penaloza@bu.edu.ORCID 0000-0003-1200-5936
Maria DekhtyarAphasia Research Laboratory, Department of Speech, Language and Hearing Sciences, Boston University, Boston, Massachusetts, USA.ORCID 0000-0002-7365-6508
Michael ScimecaAphasia Research Laboratory, Department of Speech, Language and Hearing Sciences, Boston University, Boston, Massachusetts, USA.ORCID 0000-0002-1683-7767
Erin CarpenterAphasia Research Laboratory, Department of Speech, Language and Hearing Sciences, Boston University, Boston, Massachusetts, USA.ORCID 0000-0002-7958-6803
Nishaat MukadamAphasia Research Laboratory, Department of Speech, Language and Hearing Sciences, Boston University, Boston, Massachusetts, USA.ORCID 0000-0001-9674-3519
Swathi KiranAphasia Research Laboratory, Department of Speech, Language and Hearing Sciences, Boston University, Boston, Massachusetts, USA.ORCID 0000-0003-1586-8913
Boston University · US

Funding

Predicting rehabilitation outcomes in bilingual aphasia using computational modelingU01DC014922 · NIDCD · BOSTON UNIVERSITY (CHARLES RIVER CAMPUS) · PI KIRAN, SWATHI, MIIKKULAINEN, RISTO · 2016 to 2020
$3.5M
NIDCD NIH HHS U01 DC014922
6 · The paper itself

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.

Indexed as

AphasiaBostonHumansMassachusettsProspective StudiesRandomized Controlled Trials as TopicTreatment Outcomeadult neurologyanomia therapybilingual aphasiaclinical trialscomputational modelinginternal medicineneurologyrandomized controlled trialrehabilitationrehabilitation medicinesemantic treatmentstroketherapeutics

Identifiers

PMID33208330
PMCPMC7677370
OpenAlexW3104989380

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

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.