A Review of the Comparison of Working Memory Performance, Cognitive Function, and Behavioral, and Psychological Symptoms across Normal Aging, Mild Cognitive Impairment, and Alzheimer's Disease
Bibliographic record
Abstract
This study explores the roles of working memory, cognitive functions, and behavioral and psychological symptoms in the contexts of aging, mild cognitive impairment (MCI), and Alzheimer's disease (AD). Employing a systematic review approach, insights were synthesized from diverse research perspectives. Furthermore, we aimed to investigate the association between changes in brain metabolism and cognitive score in ADNI dataset. Key findings indicate that assessment of recognition memory performance serves as a critical indicator for identifying MCI and tracking its progression to AD. Additionally, evaluating spatial working memory performance proves essential in monitoring advancement from MCI to AD stages. Furthermore, the study underscores that trends in performance on the Digit Symbol Substitution Test and the Sequencing Test among healthy adults, those with MCI, or dementia tend to converge around the age of 100. In instances of accelerated aging, neuronal loss varies across different cell groups and brain regions. The research concludes that in individuals experiencing mild to severe cognitive impairment, performance in balance, strength, and aerobic fitness correlates closely with working memory, while showing no significant association with episodic memory.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".