Variability and Coupling of Olfactory Identification and Episodic Memory in Older Adults
Bibliographic record
Abstract
OBJECTIVES: To determine whether assessment-to-assessment fluctuations in episodic memory (EM) reflect fluctuations in olfaction over time. METHODS: Within-person coupled variation in EM and the Brief Smell Identification Test (BSIT) was examined in 565 participants aged 58-106 with autopsy data from the Rush Memory and Aging Project. A growth model for up to 15 years of EM data, with BSIT as time-varying covariate, was estimated accounting for main effects of sex, education, ε4 allele, and Alzheimer's disease (AD) pathology, BSIT and time-varying BSIT, as well as the interaction between AD pathology and time-varying BSIT. RESULTS: Individuals with higher BSIT scores (b = .01, standard error [SE] = .004, p = .009) had slower declines in EM. High AD pathology (b = -.06, SE = .02, p = .001) was associated with more rapid declines in EM. The association between time-specific fluctuations in EM and BSIT differed by level of AD pathology (b = .08, SE = .034, p = .028), with a higher EM-BSIT association at higher levels of pathology. DISCUSSION: BSIT and EM fluctuate together over measurement occasions, particularly for individuals with AD pathology. Repeated intraindividual measurements provide information that could lead to early detection and inexpensive monitoring of accumulating AD pathology.
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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 | 0.007 |
| 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 | 0.001 |
| 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".