Intraindividual Variability as a Marker of Neurological Dysfunction: A Comparison of Alzheimer's Disease and Parkinson's Disease
Bibliographic record
Abstract
Individuals with certain neurological conditions may demonstrate greater inconsistency (i.e., intraindividual variability) on cognitive tasks compared to healthy controls. Several researchers have suggested that intraindividual variability may be a behavioral marker of compromised neurobiological mechanisms associated with aging, disease, or injury. The present study sought to investigate whether intraindividual variability is associated with general nervous system compromise, or rather, with certain types of neurological disturbances by comparing healthy adults, adults with Alzheimer's disease (AD), and Parkinson's disease (PD). Participants were assessed on four separate occasions using measures of reaction time and memory. Results indicated that inconsistency was correlated with indices of severity of impairment suggesting a dose-response relationship between cognitive disturbance and intraindividual variability: the more severe the cognitive disturbance, the greater the inconsistency. However, participants with AD were more inconsistent than those with PD, with both groups being more variable than the healthy group, even when controlling for group differences in overall severity of cognitive impairment or cognitive decline. Consequently, intraindividual variability may index both the severity of cognitive impairment and the nature of the neurological disturbance.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".