Effects of computer assisted training combined with the actual environment training on vascular cognitive impairment with no dementia after stoke
Bibliographic record
Abstract
Objective To observe the effects of computer assisted training combined with the actual environment training on vascular cognitive impairment with no dementia (VCIND)after stoke. Methods Sixty elderly patients with VCIND after stroke were randomly divided into a research group and a control group, each of 30. All patients were given routine medication and rehabilitation training, while the research group was additionally provided with computer assisted training and the actual environment training lasting two months. All patients' cognitive function and activities of daily life (ADL) in both groups were assessed using the Montreal cognitive assessment scale(MoCA) and modified Barthel index(MBI) before and after treatment. Results After two months of treatment, the total score of MoCA(22.80±4.63) and the scores of seven subprojects including visual space(4.00±0.93), naming (2.67±0.62), attention(4.13±0.74), language(2.33±0.62), abstract thinking(1.60±0.83), delayed memory(2.93±0.70) and orientation(5.13±1.19) in the research group were significantly higher than those before treatment and those of the control group. However, in the control group, only the total score and the scores of naming, language and delayed memory in the control group significantly were higher than those before treatment. After treatment, the scores of MBI in both group were significantly promoted, reaching (61.53±7.13) and (52.20±4.93) for the research and control group respectively, with the former improving more significantly than the latter. Conclusion The computer assisted training combined with the actual environment training helps to improve cognitive function and ADL for patients with VCIND after stoke. Such combined therapy is worth of promoting in clinical practice. Key words: Vascular cognitive impairment no dementia; The Montreal cognitive assessment scale; Computer assisted training; Actual environment training
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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".