Clinical utility of self- and informant-reported memory, attention, and spatial navigation in detecting biomarkers associated with Alzheimer disease in clinically normal adults
Bibliographic record
Abstract
Abstract Objective: Preclinical Alzheimer disease (AD) has been associated with subtle changes in memory, attention, and spatial navigation abilities. The current study examined whether self- and informant-reported domain-specific cognitive changes are sensitive to AD-associated biomarkers. Method: Clinically normal adults aged 56–93 and their informants completed the memory, divided attention, and visuospatial abilities (which assesses spatial navigation) subsections of the Everyday Cognition Scale (ECog). Reliability and validity of these subsections were examined using Cronbach’s alpha and confirmatory factor analysis. Logistic regression was used to examine the ability of ECog subsections to predict AD-related biomarkers (cerebrospinal fluid (CSF) ptau181/Aβ42 ratio (N = 371) or hippocampal volume (N = 313)). Hierarchical logistic regression was used to examine whether the self-reported subsections continued to predict biomarkers when controlling for depressive symptomatology if available (N = 197). Additionally, logistic regression was used to examine the ability of neuropsychological composites assessing the same or similar cognitive domains as the subsections (memory, executive function, and visuospatial abilities) to predict biomarkers to allow for comparison of the predictive ability of subjective and objective measures. Results: All subsections demonstrated appropriate reliability and validity. Self-reported memory (with outliers removed) was the only significant predictor of AD biomarker positivity (i.e., CSF ptau181/Aβ42 ratio; p = .018) but was not significant when examined in the subsample with depressive symptomatology available (p = .517). Self-reported memory (with outliers removed) was a significant predictor of CSF ptau181/Aβ42 ratio biomarker positivity when the objective memory composite was included in the model. Conclusions: ECog subsections were not robust predictors of AD biomarker positivity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".