Alzheimer disease biomarkers are associated with decline in subjective memory, attention, and spatial navigation ability in clinically normal adults
Bibliographic record
Abstract
Abstract Objective: Subtle changes in memory, attention, and spatial navigation abilities have been associated with preclinical Alzheimer disease (AD). The current study examined whether baseline AD biomarkers are associated with self- and informant-reported decline in memory, attention, and spatial navigation. Method: Clinically normal (Clinical Dementia Rating Scale (CDR®) = 0) adults aged 56–93 (N = 320) and their informants completed the memory, divided attention, and visuospatial abilities (which assesses spatial navigation) subsections of the Everyday Cognition Scale (ECog) annually for an average of 4 years. Biomarker data was collected within (±) 2 years of baseline (i.e., cerebrospinal fluid (CSF) p-tau181/Aβ42 ratio and hippocampal volume). Clinical progression was defined as CDR > 0 at time of final available ECog. Results: Self- and informant-reported memory, attention, and spatial navigation significantly declined over time (ps < .001). Baseline AD biomarkers were significantly associated with self- and informant-reported decline in cognitive ability (ps < .030), with the exception of p-tau181/Aβ42 ratio and self-reported attention (p = .364). Clinical progression did not significantly moderate the relationship between AD biomarkers and decline in self- or informant-reported cognitive ability (ps > .062). Post-hoc analyses indicated that biomarker burden was also associated with self- and informant-reported decline in total ECog (ps < .002), and again clinical progression did not significantly moderate these relationships (ps > .299). Conclusions: AD biomarkers at baseline may indicate risk of decline in self- and informant-reported change in memory, attention, and spatial navigation ability. As such, subjectively reported decline in these domains may have clinical utility in tracking the subtle cognitive changes associated with the earliest stages of AD.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".