Impact of Mixed Cognitive Intervention Training on Early Onset Dementia
Bibliographic record
Abstract
OBJECTIVES: The aim of this study was to investigate the impact of mixed cognitive intervention training using spaced retrieval training, and errorless learning in participants with early onset dementia. This was based on reality orientation therapy for cognitive function, depression, and occupational performance of patients. METHODS: Two early onset vascular dementia patients (> 65 years) with mild or moderate impairment were enrolled in a pre-test - post-test single-subject research design study. Prior to the study, the caregivers were interviewed about meaningful times, people, places, and areas of interest for the participant. A list of individual training words were selected based upon this information, and the participant was instructed to recall them after a 45-second, 90-second, 6-minute, and 12-minute delay. Baseline (3 sessions), intervention (20 sessions), and a second baseline period (3 sessions) were conducted. Activities of daily living were measured, and cognition was measured using the Consortium to Establish a Registry of Alzheimer's Disease Korean version, whilst depression was measured using the Korean Form Geriatric Depression Scale, and task performance and satisfaction measured by the Canadian Occupational Performance Measure. RESULTS: After intervention, both participants showed improvements in activities of daily living (ADL), word list memory/recognition, trail making A, occupational performance, and satisfaction improvement, which was clinically significant in 1 participant who also had a reduced score in the scale of depression classifying him as not depressed. CONCLUSION: Spaced retrieval training and errorless learning based on reality orientation therapy is an effective intervention in patients with early onset dementia and mild or moderate impairment.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".