The effects of cognitive training on mild-to-moderate Alzheimer＇s disease patients
Bibliographic record
Abstract
Objective To determine the efficacy of individual cognitive training （CT） in the treatment of cognitive and neuropsychiatric symptoms in patients with mild to moderate Alzheimer disease （ AD）. Methods A randomized, controlled, rater-blind clinical trial recruited 32 AD patients. AH patients were assigned to a CT group （n = 16） or a control group （a time and attention control, n = 16） for 10 weeks. All outcome measures were administered at baseline and follow-up. The cognitive status was evaluated using the Mini Mental State examination （MMSE） , a clock-drawing test （CDT） , Fuld＇s object memory evaluation （FOME） , a rapid verbal retrieval （RVR） , digit span assessments （DS） , block designing （BD）, and the A version of the trail making test （TMTA）. The patients＇ functional status was evaluated using an activities of daily living （ADL） scale. Any psychological and behavioural disorders were evaluated with the Neuropsychiatry Inventory （ NPI）. Results Patients receiving CT showed greater average improvements in NPI total score, TMT-A score and MMSE total score than the controis at week 10. There was no statistically significant benefit for CT-treated patients in terms of ADL score. Conclusions Cognitive training can raise the NPI total scores and MMSE total scores of patients with mild to moderate AD. Key words: Alzheimer＇s disease; Cognitive training; Randomized controlled trials
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".