Effect of motor imagery therapy on cognitive function of patients with stroke
Bibliographic record
Abstract
Objective To explore the rehabilitation effect of motor imagery therapy on cognitive function of stroke patients. Methods A total of 99 stroke patients with mild to moderate cognitive dysfunction were randomly divided into 3 groups: control group (N = 33), cognitive training group (N = 33) and motor imagery training group (N = 33). All patients received conventional rehabilitation training. Before and after 8-week training, all subjects were assessed with Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA). At the same time, event-related potential (ERP) was examined to detect P300 latency and amplitude. Results ompared with before training, MMSE (P = 0.000) and MoCA (P = 0.000) scores were significantly increased, P300 latency was shortened (P = 0.000) and P300 amplitude was increased (P = 0.000) in 3 groups after 8 - week training. There were significant differences among 3 groups on MMSE (P = 0.030) and MoCA (P = 0.013) scores, P300 latency (P = 0.004) and P300 amplitude (P = 0.009) before and after training. Among them, cognitive training group and motor imagery training group had significantly higher MMSE (P = 0.019, 0.021) and MoCA (P = 0.003, 0.031) scores, shorter P300 latency (P = 0.020, 0.003) and higher P300 amplitude (P = 0.003, 0.002) than control group. Conclusions Motor imagery training can not only improve motor function of stroke patients, but also improve their cognitive function. DOI: 10.3969/j.issn.1672-6731.2017.06.005
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 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".