Memory and aging: What is the real impact of age?
Bibliographic record
Abstract
Goals. Memory impairment is one of the types of cognitive impairment that most affects the elderly. Age is considered one of the major factors in memory impairment, including by the elderly themselves. Research has shown that there are other factors affecting memory of elderly persons. It remains, however, unclear what is the real impact of age in memory when controlling the influence of other variables. Thus, this study aims to analyze the impact of age on memory functioning of elderly persons and check if the potential impact remains when controlling the role of other variables (sex, education, profession, marital status, residential status, and clinical situation).Methods. The global sample comprised 1126 subjects (283 men and 843 women, 226 residents in the community and 900 institutionalized elderly) aged from 60 to 100 years. The assessment included items from the Mini-Mental State Examination (working memory), the Montreal Cognitive Assessment factor (verbal declarative memory), and Rey-Osterrieth Complex Figure (visuospatial memory).Results. Overall, age, education, profession, marital, residential, and clinical condition have differently influenced memory, depending on the type of memory. The hierarchical regression analysis showed that age is a predictive factor in all types of memory. However, other predictors have emerged with higher regression coefficients compared to age, according to the type of memory (except in working memory).Conclusions. Age, education and profession influence memory, as well as factors that potentially stimulate cognitively and socially (like having a partner and living in the community). The results indicate the importance of intervening, especially among institutionalized elderly, older, unmarried, with low education, and manual profession.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".