Understanding heterogeneity in older adults: Latent growth curve modeling of cognitive functioning
Bibliographic record
Abstract
Background: Clarifying relationships between specific neurocognitive functions in cognitively intact older adults can improve our understanding of mechanisms involved in cognitive decline, which may allow identification of new opportunities for intervention and earlier detection of those at increased risk of dementia.Method: The present study employed latent growth curve modeling to longitudinally examine the relationship between executive attention/processing speed, episodic memory, language, and working memory functioning utilizing the neuropsychological test battery from the National Alzheimer’s Disease Coordinating Center. A total of 691 relatively healthy older adults (Mage = 69.07, SD = 6.49) were assessed at baseline, and 553 individuals completed three visits spanning a two-year period.Results: Better cognitive performance was concomitantly associated with better functioning across domains. Subtle declines in executive attention/processing speed processes were found, while, on average, memory and language performance improved with repeated testing. Lower executive attention/processing speed performance at baseline predicted less incremental growth rate in memory. In turn, higher initial memory functioning was associated with incremental improvements in language performance.Conclusions: These results are consistent with the notion that intact executive function and attention processes are important to preserving memory functioning with advanced age, but are also the functions most susceptible to decline with age. These findings also provide further insight into the critical role of practice effects in clinical assessment practice and have implications for pharmaceutical trials. Practice effects should be routinely considered as they may give the appearance of retention of function within the cognitive domains considered to be a hallmark of Alzheimer’s disease pathology.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".