Application effects of WeChat platform in improving cognitive function training in patients with mild Alzheimer's disease after discharge from hospital
Bibliographic record
Abstract
Objective To explore the effects of WeChat platform in improving cognitive function training in patients with mild Alzheimer's disease (AD) after discharge from the hospital, and to slow down the disease progression, and to improve their self-care ability and the quality of life. Methods The WeChat group was established, ninety patients with mild AD were divided into the experimental group and the control group. The control group regularly issued health education knowledge in the WeChat. On the basis of control group, the experimental group published daily training plans through WeChat, They tried to complete the practice with their families' help and give feedbacks for those training videos or photos. The nurses were responsible for giving guidance and periodic support. The compliance, activities of daily living (ADL) , mini-mental state examination (MMSE) , Montreal cognitive assessment scale (MoCA) , and the world health organization quality of life measurement scale short form (WHOQOL-BREF) were compared before and after the intervention. Results In the experimental group, the cognitive level was slightly improved, and the self-care ability and the quality of life were significantly improved after the training intervention compared with the scores before the intervention (P<0.05) . However, the cognitive level, the self-care ability, and the quality of life in the control group sustained or slightly reduced after the intervention. Conclusions The cognitive function training based on WeChat platform can delay the disease development and can improve the self-care ability and the quality of life in patients with mild AD after discharge from the hospital. Key words: Alzheimer's disease; Quality of life; Cognitive training; WeChat
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 | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".