Effects of computerized cognitive training on cognitive function, activity, and participation in individuals with stroke: A randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: Computerized cognitive training (CCT) is an emerging alternative intervention for stroke survivors. OBJECTIVE: This study investigated the effects of CCT on the cognition, activity, and participation of stroke survivors and compared the findings with those of match-dosed conventional cognitive training. METHODS: This randomized controlled trial included 39 patients with stroke who were divided into the intervention group (n = 19; receiving CCT with Lumosity software) and the control group (n = 20; receiving conventional cognitive training). Both the groups were trained for 20 min, twice a week, for 12 weeks. Participants were evaluated at pretest, posttest, and 4-week follow-up. Outcome measures included various cognitive function tests and the Stroke Impact Scale scores. RESULTS: The CCT group exhibited significant improvement in global cognitive function (evaluated using the Mini-Mental State Examination and Montreal Cognitive Assessment) and specific cognitive domains: verbal working memory (backward digit span test), processing speed (Symbol Digit Modalities Test), and three MoCA subtests (attention, naming, and delayed recall). CCT exerted no significant effect on activities and participation. No significant between-group differences in changes in cognitive function were noted. However, CCT significantly improved cognitive function domains immediately after training, and these effects were sustained at the 4-week follow-up. CONCLUSIONS: Cognitive function of individuals with chronic stroke could improve after administration of CCT. However, future studies with a more rigorous design and higher training dose are warranted to validate our findings.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".