FORESHADOWING ALZHEIMER’S: VARIABILITY AND COUPLING OF OLFACTION AND COGNITION
Bibliographic record
Abstract
The earliest stage of Alzheimer’s disease (AD) pathology begins in one of the main components of the olfactory pathway, the entorhinal cortex, making deficits in smell a potential prospective biomarker for the early detection of AD. A bivariate longitudinal coupling model was used to determine whether assessment-to-assessment variation in olfaction mirrors variation in cognition over time. The model included terms for age, sex, education, ApoE e4 allele, and autopsy diagnosed AD pathology. Using a sub-sample of 573 individuals: the between-person variation in odour identification had a robust positive association to episodic memory (b = 0.129, SE = 0.0135, P < 001; conditional R-squared = 0.86). Higher AD pathology was related to both lower episodic memory at baseline (b = -0.236, SE = 0.0685, P < 001), and faster declines in episodic memory (b = -0.059, SE = 0.017, P < 001). Additionally, more rapid declines in olfactory identification were robustly associated with more rapid declines in episodic memory scores (b = 0.011, SE = 0.0039, P < 0.001). The within-person coupling between olfaction and episodic memory was robust and positive (b = 0.07, SE = 0.016, P < 001), indicating that odour identification and episodic memory scores fluctuated together over time. This research indicates that at a given occasion, individuals with higher olfactory scores also have higher episodic memory scores. This coupled relationship indicates that olfactory testing can be a useful tool for assessing cognitive decline and possibly an inexpensive screener for pathological brain changes.
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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 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".