Intraindividual variability measured with dispersion across diagnostic groups in a memory clinic sample
Bibliographic record
Abstract
Increased intraindividual variability (IIV) has been linked to outcomes such as cognitive decline and dementia, suggesting IIV might add valuable diagnostic information beyond traditional neuropsychological interpretation. We explored whether a subtype of IIV, dispersion, can provide additional information for dementia diagnosis. In a sample of memory clinic patients, three cognitive status groups were identified: subjective cognitive impairment (SCI; n = 85), amnestic mild cognitive impairment (a-MCI; n = 16), and dementia due to Alzheimer’s disease (AD; n = 48). Dispersion was computed as intraindividual standard deviations across multiple neuropsychological measures within three cognitive domains (executive functioning; immediate and delayed memory) and was compared for each diagnostic group using profile analysis. Patients with AD and a-MCI demonstrated less dispersion than patients with SCI in delayed memory. Results support existing theoretic perspectives on cognitive variability and age-related cognitive decline but suggest floor effects underlie suppression of dispersion in amnestic cognitive presentations. Questions remain about the contribution of IIV beyond impressions of impairment versus no impairment in these constrained representations of cognitive domains. Future investigations should investigate variability in SCI groups against controls to examine whether observed dispersion similarities between SCI and a-MCI or AD in immediate memory and executive functioning are meaningful.
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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 | 0.002 | 0.009 |
| 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 | 0.000 |
| 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".