Cognitive Training in a Large Group of Patients Affected by Early-Stage Alzheimer's Disease can have Long-Lasting Effects: A Case-Control Study
Bibliographic record
Abstract
Introduction: Cognitive training in Alzheimer's disease (AD) has recently started to demonstrate its efficacy. We used our ‘puzzle-like’ task (GEO) as training for a large group of early-stage AD patients, to detect its effects over time. Method: AD patients (N = 40) and healthy controls (N = 40) were involved. Participants were administered the Geographical Exercises for cognitive Optimization (GEO) task. Participants underwent individual sessions with GEO three times a week for 2 months, and then their performance was recorded again. Lastly, at the 12-month follow-up the GEO task was administered for the last time. Results: Patients’ scores were significantly worse than controls’ scores only on a few neuropsychological tests. We ran a repeated measures GLM by considering groups’ performance on the GEO task at the assessment points. Results showed a significant main effect of group, and a significant effect of the interaction between group and time: patients’ performances both at the end of the training and at the follow-up were virtually identical to controls’ performances. Conclusions: Patients effectively acquired new procedural abilities, and their achievements were stable at follow-up. This study suggests the GEO is a useful strategy for cognitive training in AD, and should prompt further investigations about the degree of generalisability of patients’ acquired skills.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".