Effectiveness of computer-assisted cognitive training on cognitive function and activities of daily living in patients with post-stroke cognitive impairment
Bibliographic record
Abstract
Objective: This study aimed to investigate the effects of computer-assisted cognitive training (CACT) on cognitive function and activities of daily living in patients with post-stroke cognitive impairment. Additionally, it aimed to explore the changes in specific cognitive domains before and after treatment. Design: The study was a double-blind, randomized, controlled trial. Setting: It took place in rehabilitation wards or outpatient clinics. Participants: Sixty patients with post-stroke cognitive impairment took part in the study. Interventions: = 30). Both groups received conventional rehabilitation and cognitive training, and the intervention group additionally received CACT. Main outcome measures: The primary outcome measures included the Mini-Mental State Examination (MMSE) and event-related potential (ERP) P300 for cognitive function, as well as the modified Barthel Index (MBI) for activities of daily living. Secondary outcomes were the Trail Making Test (TMT), the Symbol-Digit Modalities Test (SDMT), the Auditory Verb Learning Test-Huashan version (AVLT-H), the Boston Naming Test (BNT), and the Clock Drawing Test (CDT), which assessed cognitive function across specific domains. Results: Both groups showed significant improvements in MMSE, MBI, amplitude of P300, and latency of P300, with the intervention group demonstrating more pronounced improvements compared to the control group. In terms of specific cognitive domains, the intervention group exhibited greater improvements than the control group in TMT-A, TMT-B, and AVLT-H. Both groups showed improvements in SDMT and BNT, but the differences between the groups were not statistically significant. Additionally, there was no significant improvement in the CDT for either group before and after treatment. Conclusion: CACT was found to improve patients' cognitive function, especially in areas of attention, executive function, and memory. It also effectively improved activities of daily living.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".