2220 – Associative Memory Exercises As a Means For Alzheimer's Treatment: a Case Study
Bibliographic record
Abstract
People living with Alzheimer's disease experience a gradual decline in cognitive skills, especially in memory (Selkoe, 2002). However, through the frequent and regular use of memory exercises a cognitive improvement may be achieved. The intention of this paper is to report the results of an 8-weeks associative memory exercise regime applied to an Alzheimer's participant. Our hypothesis is that exercising the associative memory regularly and frequently will slow the Alzheimer's progression, and may even improve the mental and cognitive condition. To test our hypothesis, we used our recently designed associative memory exercises described in (Garcia et. al, 2012). An 86 years old female, diagnosed with Alzheimer's disease at relatively early stages, participated in this study (during 8 consecutive weeks, with 3 exercise sessions/week). At the beginning, at the end of the exercise regime and one month afterwards, the Wechsler Memory Scale questionnaire (WMS-III) was employed to assess her memory and mental state. Results from the memory exercises showed that the participant's performance improved over the eight weeks of trials. Moreover, 4 out of the 12 WMS-III subtests that were related to associative memory the most, presented score increments at the end of the exercise program (Logical Memory-I (0 to 7), Logical Memory-II-Recognition (15 to 20), Verbal Paired Associates-II-Recognition (13–16) and Family Pictures-II (4–11)). These results are as encouraging as the ones found in (Garcia et.al, 2012); they suggest that the designed memory exercises may be used as an effective tool to improve the cognitive state of individuals with 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".