Assessing how Age, Sex, Race, and Education Affect the Relationships Between Cognitive Domains and Odor Identification
Bibliographic record
Abstract
BACKGROUND: The associations between cognitive domains and odor identification are well established, but how sociodemographic variables affect these relationships is less clear. PURPOSE: Using the survey-adapted Montreal Cognitive Assessment instrument (MoCA-SA), we assess how age, sex, race, and education shape these relationships. METHODS: We first used cluster analysis and multidimensional scaling to empirically derive distinct cognitive domains from the MoCA-SA as it is unclear whether the MoCA-SA can be disaggregated into cognitive domains. We then used ordinal logistic regression to test whether these empirically derived cognitive domains were associated with odor identification and how sociodemographic variables modified these relationships. STUDY POPULATION: Nationally representative sample of community-dwelling US older adults. RESULTS: We identified 5 out of the 6 theoretical cognitive domains, with the language domain unable to be identified. Odor identification was associated with episodic memory, visuospatial ability, and executive function. Stratified analyses by sociodemographic variables reveal that the associations between some of the cognitive domains and odor identification varied by age, sex, or race, but not by education. CONCLUSIONS: These results suggest that (1) the MoCA-SA can be used to identify cognitive domains in survey research and (2) the performance of smell tests as a screener for cognitive decline may potentially be weaker in certain subpopulations.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".