The Efficacy of COGnitive tRaining in patiEnts with Amnestic mild coGnitive impairmENT (COG-REAGENT): Protocol for a Multi-Center Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Amnestic mild cognitive impairment (aMCI) is often the prodromal stage of Alzheimer's disease (AD). Although previous studies have suggested that computerized cognitive training is an effective non-pharmacological intervention for aMCI, large-sample, randomized controlled studies are warranted to provide a high level of evidence. OBJECTIVE: To identify the efficacy of computerized cognitive training for aMCI. METHODS: This study will include 260 patients diagnosed with aMCI from 8 centers in China. A computerized multi-domain adaptive training program will be used in this study, and the targeted cognitive domains include memory, attention, language, and executive function. The patients will be randomized into either a cognitive-training group or an active-control group. The intervention is a 12-week internet-based cognitive training performed for 40 minutes per day, 4 days a week. Neuropsychological assessments and structural and functional MRI will be obtained at baseline, at the end of the intervention, and 6 months after randomization. The primary outcome will be the global cognitive function score assessed by Montreal Cognitive Assessment. The secondary outcomes include changes in other neuropsychological assessments and neuroplasticity changes measured by structural and functional MRI. RESULTS: The trial is currently ongoing, and it is anticipated that recruitment will be completed in December 2020. CONCLUSION: This multi-center, large-sample, randomized controlled trial will investigate the short and long-term effects of computerized cognitive training in patients with aMCI. Furthermore, the combination of functional and structural MRI results will also reveal the underlying mechanisms of the effect of intervention.
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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 | 0.042 | 0.030 |
| Meta-epidemiology (narrow) | 0.008 | 0.004 |
| Meta-epidemiology (broad) | 0.015 | 0.007 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.047 | 0.008 |
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".