Memory decline in older individuals predicts an objective indicator of oral health: findings from the Sydney Memory and Ageing Study
Bibliographic record
Abstract
BACKGROUND: Growing evidence suggests that there is an association between poor oral health and cognitive function in late adulthood. However, most studies to date have relied on cross-sectional research methods that do not permit inferences about the temporality of any association. Moreover, the few longitudinal studies that do exist have typically relied on small samples and quite limited cognitive or oral health assessments. The aim of the present study was therefore designed to provide the first direct evaluation of whether cognitive function is predictive of poor oral health in older adults. METHODS: This longitudinal research included data from 339 participants aged 70 years or older from The Sydney Memory and Ageing Study (MAS), a large cohort of healthy community-dwelling older adults. Cognitive function was assessed using a battery of tests at baseline (Wave 1) in 2005 and six years later (Wave 4) in 2011. In 2015 (Wave 6), participants were assessed for oral health using the Oral Health Assessment Tool (OHAT), number of functional occluding pairs of natural teeth and sublingual resting saliva pH (SRSpH). Ordinal least squares regression analysis was used to model the effect of cognitive function on total OHAT score, and binomial logistic regression used for SRSpH and occluding pairs of functional teeth. RESULTS: Two models were tested. In the partially adjusted model, age, gender and years of education were included. The fully adjusted model additionally included medical conditions, general health, depression, smoking, alcohol consumption, functionality, and dental care utilization. The key finding to emerge was that a six-year change in memory (from Wave 1 to Wave 4) was associated with lower sublingual resting saliva pH at Wave 6 in partially (Odds Ratio (OR) = 0.65) and fully adjusted model (OR = 0.63). CONCLUSIONS: This longitudinal study provides further evidence that a relationship between cognitive function and oral health exists, and also points to this relationship potentially being bi-directional, as previous evidence suggests. The findings from the study also suggest that older adults who present with greater than normal memory decline at an earlier point in life were more likely to experience poor oral health when this was evaluated at a later time-point, four years later.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".