Study partner‐reported decline identifies cognitive decline and dementia risk
Bibliographic record
Abstract
OBJECTIVE: Identifying individuals at risk for cognitive decline, Mild Cognitive Impairment (MCI), and dementia due to Alzheimer's disease (AD) is a critical need. Functional decline is associated with risk and can be efficiently assessed by participants and study partners (SPs). We tested the hypothesis that SP-reported functional decline is an independent predictor of dementia risk and cognitive decline. METHODS: In 1048 older adults in the Alzheimer's Disease Neuroimaging Initiative (ADNI), we measured associations between Everyday Cognition Scale scores (ECog, self- and SP-reported versions) and (1) baseline and longitudinal change in neuropsychological test (NPT scores) across multiple cognitive domains; (2) diagnostic conversion to MCI or dementia. Models included Mini Mental Status Exam (MMSE) score and ApoE ε4 genotype (APOE) as predictors. Model fits were compared with and without predictors of interest included. RESULTS: SP-reported ECog was the strongest predictor of cognitive decline across multiple domains, as well as diagnostic conversion. Self-reported ECog was associated with baseline NPT scores in some cognitive domains, and diagnostic conversion to MCI in participants with biomarker evidence for AD (elevated brain β-amyloid, Aβ). Models including SP-reported ECog were significantly stronger at predicting outcomes. CONCLUSIONS: SP-reported functional decline is an independent indicator of cognitive decline and dementia risk, even when accounting for cognitive screening, genetic risk, demographics, and self-report decline. The results provide a rationale for greater utilization of SP-reported functional decline to identify those at risk for dementia due to AD and other causes.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".