Odor Identification Screening Improves Diagnostic Classification in Incipient Alzheimer’s Disease
Bibliographic record
Abstract
BACKGROUND: Measurements of olfaction may serve as useful biomarkers of incipient dementia. Here we examine the improvement in diagnostic accuracy of Alzheimer's disease (AD) and mild cognitive impairment (MCI) when assessing both cognitive functioning and odor identification. OBJECTIVE: To determine the utility of odor identification as a supplementary screening test in incipient AD. METHODS: Sniffin' Sticks Odor Identification Test (SS-OIT) and the Montreal Cognitive Assessment (MoCA) were administered in 262 AD, 174 MCI [150 amnestic (aMCI), and 24 non-amnestic (naMCI)], and 292 healthy older adults (HOA). RESULTS: Odor identification scores were higher in HOA relative to MCI or AD groups, and MCI outperformed AD. Odor identification scores were higher in aMCI single domain than aMCI multiple domain. Complementing MoCA scores with the SS-OIT significantly improved diagnostic accuracy of individuals with AD and MCI, including within MCI subgroups. DISCUSSION: Odor identification is a useful supplementary screening tool that provides additional information relevant for clinical categorization of AD and MCI, including those who are at highest risk to convert to 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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".