Age-related dynamics of cognitive functioning in healthy normals
Bibliographic record
Abstract
In light of sociodemographic changes that are increasing the representation of older adults in the labour market, studying the dynamics of cognitive ageing has become imperative. With age, the brain undergoes a series of functional and structural changes that impact professional and social activity. To identify dysfunctional cognitive domains in normals of middle, mature, pre-retirement, and elderly ages, a screening status of core cognitive functions was conducted with 187 participants: 70 males (mean age 47,78 ±9,04 years) and 117 females (mean age 47,43 ±8,62 years). All participants were volunteers with full-time employment. The samples were divided into four age groups: 30–40 years (middle age), 40–50 years (mature age), 50–60 years (pre-retirement age), and 60–68 years (elderly age). The diagnostic battery included free association tasks, the Montreal Cognitive Assessment, and the Frontal Assessment Battery. Key findings: Cognitive ageing exhibits no gender specificity, heterochronicity and multidirectional changes. Memory emerges as the most vulnerable domain, with deficits manifesting in middle age as selective retrieval difficulties. Speech fluency declines with age, accompanied by increased rigidity in associative tasks. Undamaged functions: multidimensional spatiotemporal orientation, visuospatial skills, and temporal awareness. A high educational level significantly slows cognitive decline.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.001 | 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".