Language-Specific Dual-Task Effects After Stroke: A Systematic Review
Bibliographic record
Abstract
PURPOSE: The dual-task paradigm has been frequently used to examine stroke-related deficits because it samples behavioral performance under conditions of distraction similar to functioning in real-life environments. This original systematic review synthesizes studies that examined dual-task effects involving spoken language production in adults affected by stroke, including transient ischemic attack (TIA) and poststroke aphasia. METHOD: Five databases were searched (inception to March 2022) for eligible peer-reviewed articles. The 21 included studies reported a total of 561 stroke participants. Thirteen studies focused on single word production, for example, word fluency, and eight on discourse production, for example, storytelling. Most studies included participants who had suffered a major stroke. Six studies focused on aphasia, whereas no study focused on TIA. A meta-analysis was not appropriate because of the heterogeneity of outcome measures. RESULTS: Some single word production studies found dual-task language effects whereas others did not. This finding was compounded by the lack of appropriate control participants. Most single word and discourse studies utilized motoric tasks in the dual-task condition. Our certainty (or confidence) assessment was based on a methodological appraisal of each study and information about reliability/fidelity. As 10 of the 21 studies included appropriate control groups and limited reliability/fidelity information, the certainty of the findings may be described as weak. CONCLUSIONS: Language-specific dual-task costs were identified in single word studies, especially those that focused on aphasia as well as half of the nonaphasia studies. Unlike single word studies, nearly all studies of discourse showed dual-task decrements on at least some variables. SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.23605311.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".