A Hierarchical Multi-Dimensional Cognitive Training Program for Preventive Cognitive Decline in Acute Ischemic Stroke Patients: Study Protocol for a Randomized Controlled Trial
Bibliographic record
Abstract
Background: One of the most popular ways to address cognitive decline is cognitive training. The fact that cognitive deterioration is permanent is one of the main issues. This issue might be resolved by preventive cognitive training when it is acute. As a result, this study aims to design and assess how well stroke patients respond to hierarchical, multi-dimensional preventative cognitive training. Objective: To describe the study design of this center implementation trial. Methods: Participants in the study will be recruited from a hospital in China and randomly assigned to the intervention group or the usual care group. Interventions will include four-week hierarchical multi-dimensional preventive cognitive training through a WeChat program. for Primary outcome measures will be the Montreal Cognitive Assessment, Mini-Mental State Examination, and Post-Stroke Cognitive Impairment (PSCI) Incidence. The secondary outcome measure will include the Hamilton Depression Scale, Hamilton Anxiety Scale, Modified Barthel Index, and National Institutes of Health Neurological Deficit Score. Outcomes will be measured at baseline, 12 weeks, and 24 weeks from the baseline. Results: We expect that the hierarchical multi-dimensional preventive cognitive training program will be easy to implement, and the cognitive function, cognitive psychology, ability of daily living will vary in each setting. Conclusions: The results will provide evidence highlighting differences in a new strategy of cognitive training through the WeChat program, which allows the home-based practice, puts forward an advanced idea of preventive cognitive training in the acute stage, and has the highest effectiveness of reducing cognitive impairment, and Alzheimer's disease.
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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 | 0.025 | 0.020 |
| Meta-epidemiology (narrow) | 0.007 | 0.003 |
| Meta-epidemiology (broad) | 0.018 | 0.007 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.060 | 0.009 |
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".