Cognitive Function and Olfactory Impairment in Community-Dwelling Older Adults Attending a Salon
Bibliographic record
Abstract
BACKGROUND: Early detection, treatment, and care are important for the prevention of dementia. Though olfactory impairment has been reported to be associated with mild cognitive impairment (MCI), there are few reports involving community-dwelling older adults with no difficulty in daily life in Japan. OBJECTIVE: This study aimed to identify the frequency of MCI in community-dwelling elderly people attending community salons in a city with a high aging population, and to determine the usefulness of a pocket olfactory discrimination test. METHODS: We recruited 268 independent older adults attending community salons. The Japanese version of the MCI Screen was used to evaluate cognitive function, defining MCI as <49.8 out of 100 scores of the memory performance index (MPI). The Japanese version of the University of Pennsylvania Smell Identification Test (UPSIT-J) was used for olfactory discrimination and the number of correct answers out of 4 questions was noted. RESULTS: Of the salon participants, 138 (51.5%) were classified as having MCI. As the number of correct answers on the olfactory test decreased, the percentage of participants with MCI increased. The MPI score of the low UPSIT score group was significantly lower than that of the high UPSIT score group. CONCLUSIONS: This study revealed that approximately half of the elderly local residents living without daily problems were suspected of having MCI. The UPSIT-J-4 is a simple olfactory identification test, and can be used at any time and any place. It is useful for screening cognitive function via olfactory identification in a salon-like setting.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".