The role of executive control in post-stroke aphasia treatment
Bibliographic record
Abstract
Executive control (EC) ability is increasingly emerging as an important predictor of post-stroke aphasia recovery. This study examined whether EC predicted immediate treatment gains, treatment maintenance and generalization after naming therapy in ten adults with mild to severe chronic post-stroke aphasia. Performance on multiple EC tasks allowed for the creation of composite scores for common EC, and the EC processes of shifting, inhibition and working memory (WM) updating. Participants were treated three times a week for five weeks with a phonological naming therapy; difference scores in naming accuracy of treated and untreated words (assessed pre, post, four- and eight-weeks after therapy) served as the primary outcome measures. Results from simple and multiple linear regressions indicate that individuals with better shifting and WM updating abilities demonstrated better maintenance of treated words at four-week follow-up, and those with better common EC demonstrated better maintenance of treated words at both four- and eight-week follow-ups. Better shifting ability also predicted better generalization to untreated words post-therapy. Measures of EC were not indicative of improvements on treated words immediately post-treatment, nor of generalization to untreated words at follow-up. Findings suggest that immediate treatment gains, maintenance and generalization may be supported by different underlying mechanisms.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".