Relationship Between Cortisol Levels and Memory Performance may be Modulated by the Presence or Absence of Cognitive Impairment: Evidence from Healthy Elderly, Mild Cognitive Impairment and Alzheimer's Disease Subjects
Bibliographic record
Abstract
An inverted U-shape function between cortisol levels and memory performance has been reported in studies on both young animals and humans. Yet little is known about this relationship in normal aging or in older subjects with cognitive impairment. This issue is particularly significant since increased levels of cortisol have been reported in Alzheimer's disease (AD). The present study examined the association between cortisol levels and visual memory performance in healthy subjects as well as in individuals presenting mild cognitive impairment (MCI) or AD. Salivary cortisol was measured in 40 healthy elderly subjects, 31 individuals with amnestic MCI, and 40 subjects with mild probable AD. Memory performance was evaluated using the Brief Cognitive Screening Battery. Higher cortisol levels were associated with better memory performance in healthy elderly (p=0.005), while higher cortisol levels were correlated with poorer memory performance in MCI subjects (p=0.011). No correlation between cortisol and memory was found in the AD group (p > 0.05). These results suggest that the relationship between cortisol levels and memory performance in the aging process could vary according to the presence or absence of cognitive 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".