Cognitive dynamics in healthy aging and mild cognitive impairment
Bibliographic record
Abstract
Abstract Background In observational and clinical intervention studies of Alzheimer disease (AD), cognition is typically assessed in an extended, “single‐shot” testing session to measure mean‐level changes in performance over time. However, this strategy ignores day‐to‐day fluctuations in cognition that may occur due to mood, stress or fatigue. It is possible that the overall magnitude of daily variability in cognition is a more sensitive endpoint than traditional metrics of cognitive change. Previous research has suggested that attention and personality traits are important predictors of AD, yet it is unknown how scores on these outcomes fluctuate over short intervals. The aim of this project was to evaluate the extent to which scores on attention, personality and mood and fatigue vary across 3 weeks of daily testing and whether cognitive variability increases in early‐stage AD. Method A total of 66 younger adults (YAs), 52 cognitively healthy older adults (OAs), and 37 individuals with cognitive impairment (CIs) completed 21 daily assessments of attention, personality, and mood. Cognitive impairment was determined as a rating of 0.5 on the Clinical Dementia Rating scale or a score of less than 11 on the mini Montreal Cognitive Assessment. Group differences in mean performance and variability were assessed using Bayesian mixed effects location scale models. Result Adherence was high with an average of 20.1, 19.7 and 19.2 daily sessions (out of 21 possible) completed for YAs, OAs and CIs, respectively. YAs reported overall lower mood compared to the OAs. No other mean comparisons were significant. In terms of daily variability, YAs had greater fluctuations in reported fatigue, conscientiousness and neuroticism compared to healthy OAs. Finally, both YAs and CIs had greater variability in attention relative to OAs. These differences persisted after accounting for global cognition. Conclusion Healthy aging (OAs compared to YAs) results in greater stability in personality ratings. In contrast, pathological aging (CIs compared to OAs) results in greater variability in attention scores with no detectable changes on mean performance. We argue that cognitive variability provides unique insight to cognitive changes that occur in healthy aging and early‐stage AD and should continue to be explored as primary outcomes.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".