Evidence map›Paper›PMID 38586504›Full record

ArticleBilingualism (Cambridge, England)2024

Multilevel factors predict treatment response following semantic feature-based intervention in bilingual aphasia.

Michael Scimeca, Claudia Peñaloza, Swathi Kiran

Abstract read
In one paragraph

Article in Bilingualism (Cambridge, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed.

  1. Trial
  2. Narrative Discourse Predictors of Response to Naming Intervention in Aphasia.Journal of speech, language, and hearing research : JSLHR · 2026
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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

3 authors.

Michael ScimecaDepartment of Speech, Language, and Hearing Sciences, Boston University, MA, USA.
Claudia PeñalozaDepartment of Cognition, Development and Educational Psychology, Faculty of Psychology, University of Barcelona, Barcelona, Spain.
Swathi KiranDepartment of Speech, Language, and Hearing Sciences, Boston University, MA, USA.

Funding

Advanced Research Training in Communication Sciences and DisordersT32DC013017 · NIDCD · BOSTON UNIVERSITY (CHARLES RIVER CAMPUS) · PI Cara E. Stepp · 2016 to 2026
$4.9M
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 T32 DC013017NIDCD NIH HHS U01 DC014922
6 · The paper itself

Abstract

Semantic feature-based treatments (SFTs) are effective rehabilitation strategies for word retrieval deficits in bilinguals with aphasia (BWA). However, few studies have prospectively evaluated the effects of key parameters of these interventions on treatment outcomes. This study examined the influence of intervention-level (i.e., treatment language and treatment sessions), individual-level (baseline naming severity and age), and stimulus-level (i.e., lexical frequency, phonological length, and phonological neighborhood density) factors on naming improvement in a treated and untreated language for 34 Spanish-English BWA who completed 40 hours of SFT. Results revealed significant improvement over time in both languages. In the treated language, individuals who received therapy in their L1 improved more. Additionally, higher pre-treatment naming scores predicted greater response to treatment. Finally, a frequency effect on baseline naming accuracy and phonological effects on accuracy over time were associated with differential treatment gains. These findings indicate that multilevel factors are influential predictors of bilingual treatment outcomes.

Indexed as

anomia therapyBilingual aphasiacross-language generalizationsemantic-feature treatmenttreatment outcomes

Identifiers

PMID38586504
PMCPMC10993298

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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.