P2‐307: Intraindividual variability across neuropsychological tasks is associated with risk of Alzheimer's disease
Bibliographic record
Abstract
Recent theorizing suggests that within-person variability, across trials of an individual task as well as across a neuropsychological task profile, is linked to cognitive function (Holzter et al., 2008; Hultsch et al., 2002). In the present study, we employ data from the Kungsholmen Project (KP), a Swedish population-based longitudinal study, to evaluate whether across-task within-person variability estimates are linked to increased dementia risk independent of select confounds. Dispersion is operationalized as intraindividual variability across key neuropsychological tasks, computed as the intraindividual standard deviation (ISD) for each individual independent of mean age group differences in performance. Neuropsychological tests known to differentiate cognitively intact from impaired groups were selected, including episodic recall and recognition, block design, category and letter fluency, and Trailmaking. A total of 234 controls and 67 Alzheimer Disease (AD) cases were evaluated at cross-section. Increased variability across the neuropsychological profile was clearly linked to increased risk for AD. Individuals with AD exhibited higher dispersion values, reflecting relatively uneven performance profiles across the neuropsychological test battery. Independent of age and years of education, a per-unit increase in ISD was associated with a 12.3% increase of AD. Notably, restricting the dispersion calculations to episodic memory measures varying in level of support (recall vs. recognition, free vs. cued recall, etc) did not facilitate detection of those at increased risk of AD. Findings will be discussed in terms of the implications of dispersion for supplementing existing neuropsychological batteries, and for improving sensitivity to detect those at risk of AD. We are currently exploring the longitudinal link between changes in dispersion and dementia risk across as many as 12 years of measurement in the KP.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".