Train-your-brain pilot community-based intervention after stroke: cognitive trajectory over 10-month follow-up
Bibliographic record
Abstract
Introduction Stroke leads to cognitive impairments that affect survivors’ quality of life. This study aimed to assess the effectiveness of the Train-Your-Brain (TYB) pilot community intervention in cognitive outcomes among stroke survivors and caregivers at baseline, post-intervention, and 10-month follow-up. Methods Thirty-one participants (20 stroke survivors, 11 caregivers) were evaluated. Cognitive functioning was measured using the Montreal Cognitive Assessment (MoCA) with analysis of subtest-level performances and Symbol Digit Modalities Test (SDMT). Results Among stroke survivors, MoCA immediate recall scores maintained during the intervention, but declined 10-months later (p = 0.005). Analysis of the MoCA delayed memory subtest revealed a graded performance across different recall formats. Free recall and category-cued recall deteriorated over 10 months, while multiple-choice format recall remained stable. A slight improvement was observed in SDMT scores from pre-TYB to post-TYB, which was relatively maintained after 10 months. Caregivers demonstrated significant improvements in MoCA language and sentence repetition (p = 0.014) scores at 10-month follow-up. Conclusion Our findings suggest that while the intervention may lead to short-term stabilization in cognitive functioning among stroke survivors, these gains may not be sustained over time. Persistent cognitive deficits underscore the need for ongoing and long-term support.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".