What Influences Language Impairment in Bilingual Aphasia? A Meta-Analytic Review
Bibliographic record
Abstract
Patterns of language impairment in multilingual speakers with post-stroke aphasia are diverse: in some cases the language deficits are parallel, that is, all languages are impaired relatively equally, whereas in other cases deficits are differential, that is, one language is more impaired than the other(s). This diversity stems from the intricate structure of the multilingual language system, which is shaped by a complex interplay of influencing factors, such as age of language acquisition, frequency of language use, premorbid proficiency, and linguistic similarity between one's languages. Previous theoretical reviews and empirical studies shed some light on these factors, however no clear answers have been provided. The goals of this review were to provide a timely update on the increasing number of reported cases in the last decade and to offer a systematic analysis of the potentially influencing variables. One hundred and thirty cases from 65 studies were included in the present systematic review and effect sizes from 119 cases were used in the meta-analysis. Our analysis revealed better performance in L1 compared to L2 in the whole sample of bilingual speakers with post-stroke aphasia. However, the magnitude of this difference was influenced by whether L2 was learned early in childhood or later: those who learned L2 before 7 years of age showed comparable performance in both of their languages contrary to the bilinguals who learned L2 after 7 years of age and showed better performance in L1 compared to L2. These robust findings were moderated mildly by premorbid proficiency and frequency of use. Finally, linguistic similarity did not appear to influence the magnitude of the difference in performance between L1 and L2. Our findings from the early bilingual subgroup were in line with the previous reviews which included mostly balanced early bilinguals performing comparably in both languages. Our findings from the late bilingual subgroup stressed the primacy of L1 and the importance of age of L2 learning. In addition, the evidence from the present review provides support for theories emphasizing the role of premorbid proficiency and language use in language impairment patterns in bilingual aphasia.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.020 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".