Smell and taste recognition in early stages of late-onset Alzheimer’s disease
Bibliographic record
Abstract
Problems of inadequate nutrition and energy intake are common in the aging population. Smell and taste deficits associated with Late-Onset Alzheimer’s Disease (LOAD) may accentuate the decline in nutritional status of elderly individuals and indirectly enhance progression of cognitive problems in LOAD. The objective of this study was to explore and characterize smell and taste recognition abilities in early stages of LOAD, beyond that of normal healthy aging. A total of 29 healthy-younger subjects aged 18-40 (HY), 13 healthy-elderly (HA) and six elderly adults diagnosed with LOAD (AD) aged 60-85, were recruited from the Guelph community. The Sniffin’ Sticks Screening Test (SSST) and Taste Strips were used to test olfactory and gustatory functions, respectively. Participants also completed the mini-mental state examination (MMSE), clock test and word recall tests to assess cognitive/memory skills. Compared to HA individuals, people with AD had significant odour recognition impairment. Correlation analysis also revealed an age-associated decline in overall taste ability. When specific tastes were examined, impairments in sour and bitter identification were observed with increasing age. However, no significant differences in specific taste abilities were found between HA and AD individuals. In predicting health status (ie. presence or absence of LOAD), an assessment of all variables in this study was conducted using Generalized Linear Model (GLM). Results showed that sweet recognition and clock test scores were the best predictive variables of health status. However, this is a preliminary model that needs refinement through further research using more individuals.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".