Expanding the scope of olfactory evaluation in Alzheimer's disease and related dementias (ADRD): A narrative review of the role for odor memory and recognition in AD/ADRD
Bibliographic record
Abstract
Olfactory dysfunction (OD) is a well-characterized feature of Alzheimer's disease (AD) and is often one of the earliest functional biomarkers in the disease course. As such, olfactory evaluation shows promise as an important tool in AD screening and may provide insight into pathologic underpinnings and potential treatment pathways. The National Plan to Address Alzheimer's Disease emphasizes the importance of including Alzheimer's disease related dementias (ADRD) in research efforts, and while olfaction is also associated with ADRD, this association is relatively understudied. Additionally, there have been efforts to expand the evaluation of olfactory function to include assessments beyond key domains, such as odor threshold, odor discrimination, and odor identification, which have been the primary focus of most olfaction research in AD/ADRD. Odor recognition memory assessments have been developed primarily for their utility in identifying and stratifying individuals along the AD continuum, although a wide variety of methods have been reported in the literature. In this narrative review, we provide an overview of odor identification, odor threshold, and odor discrimination in AD/ADRD with a specific focus on providing a centralized guide detailing odor recognition memory methods and their utility in AD/ADRD.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".