Efficacy of Executive Function Interventions After Stroke: A Systematic Review
Bibliographic record
Abstract
PURPOSE: Disorders in executive functions are common post stroke and play a critical role in predicting functional recovery. To establish best practice recommendations, it is necessary to appraise the evidence regarding specific executive function interventions post stroke. This systematic review aims to determine whether executive function intervention is more effective than no or alternative intervention in improving executive functions and functional abilities in the acute, subacute, and chronic stages post stroke. METHOD: A systematic review was performed up to January 2011 of MEDLINE, CINAHL, PsychINFO, OTseeker, and Cochrane databases. Eligible studies needed to include a cognitive intervention to remediate executive function impairments post stroke or to improve functional tasks compromised by these impairments. Methodological quality of randomized trials was rated by 2 authors. The level of evidence for each intervention, according to stage of recovery, was determined. RESULTS: Ten studies met inclusion criteria - 1 evaluating treatment in the subacute and 9 in the chronic stage. Limited evidence from the 1 study in the subacute stage (level 2b) and 9 studies (including 3 randomized controlled trials) in the chronic stage (level 2a) support using remedial (eg, computerized working memory training) and compensatory interventions (eg, problem-solving strategies, paging system) for improving executive functioning and, possibly, functional abilities. CONCLUSION: These findings suggest that persons with stroke may possibly benefit from specific executive function training and learn compensatory strategies to reduce the consequences of executive impairments. Further research is needed in acute and subacute stroke, when the impact of treatment is potentially great and where few studies have been undertaken.
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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 | 0.012 | 0.050 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.011 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| 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".