Dispersion-based cognitive intra-individual variability in dementia with Lewy bodies.
Bibliographic record
Abstract
OBJECTIVE: Cognitive fluctuations are characteristic of dementia with Lewy bodies (DLB) but challenging to measure. Dispersion-based intra-individual variability (IIV-d) captures neurocognitive performance fluctuations across a test battery and may be sensitive to cognitive fluctuations but has not been studied in DLB. METHOD: We report on 5,976 participants that completed the uniform data set 3.0 neuropsychological battery (UDS3NB). IIV-d was calculated via the intra-individual standard deviation across 12 primary UDS3NB indicators. Separate models using mean USD3NB score and the Montreal cognitive assessment (MoCA) total score tested the reproducibility of the incremental value of IIV-d over-and-above global cognition. Binary logistic regressions tested whether IIV-d could classify individuals with and without clinician-rated cognitive fluctuations. Multinomial logistic regressions tested whether IIV-d could differentiate participants with DLB, participants with Alzheimer's disease (AD), and participants with healthy cognition (CH), as well as the incremental diagnostic utility of IIV-d over-and-above clinician-rated cognitive fluctuations. RESULTS: IIV-d exhibited large univariate associations with clinician-rated and non-clinician-informant reported cognitive fluctuations, which persisted when adjusting for MoCA but not the full battery mean. Of diagnostic relevance, greater IIV-d was consistently associated with DLB and AD relative to CH over-and-above global cognition and clinician-rated cognitive fluctuations. Greater IIV-d was less consistently associated with an increased probability of DLB relative to AD when controlling for global cognition. CONCLUSIONS: IIV-d accurately differentiates DLB from CH over-and-above global cognition and clinician-rated cognitive fluctuations. IIV-d may supplement a thorough clinical interview of cognitive fluctuations and serve as a standardized performance-based indicator of this transdiagnostic phenomenon. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".