The association between oral health and mild cognitive impairment in community-dwelling older adults
Bibliographic record
Abstract
Abstract Background:As the population ages, the number of older adults aged 65 and over is increasing. Increasing age is associated with an increased risk of oral disease and cognitive decline. Older adults with cognitive impairment can experience poor oral health due to reduced self-care abilities, yet the impact of various oral health indicators on the cognitive abilities of older adults remains unclear. This study sought to investigate the relationship between various oral health indicators and mild cognitive impairment (MCI) in older adults. Methods:A cross-sectional study of 234 older adults aged 65 years or over was performed between June and September 2022. This study developed a data web platform specifically to screen and intervene with older adults with MCI, using the Mini-mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), Activities of Daily Living (ADL), Clinical Dementia Rating (CDR) and Hachinski Ischemic Score (HIS) to measure MCI. Oral health status was measured by subjective and objective assessment tools, and the oral health-related quality of life (OHRQoL) was assessed by Geriatric Oral Health Assessment Index (GOHAI). Results: The univariate analyses revealed that older adults with poor oral health indicators of dental caries, chewing ability, oral and maxillofacial pain, self-perceived oral health, and OHRQoL had lower cognitive levels. The stepwise logistic regression analysis observed that higher literacy level (OR=0.064, 95%CI=0.007, 0.567) and OHRQoL score (OR=0.920, 95%CI=0.878, 0.963) were negatively associated with the presence of MCI. Conclusions:OHRQoL was found to be independently associated with MCI, implying that OHRQoL may be important in mitigating cognitive decline. The GOHAI scale can be used to more easily and reliably assess the oral health of older adults, which is important for the timely detection of poor oral conditions to delay cognitive decline. Medical workers should develop programs to improve the OHRQoL of older adults and improve the cognitive performance of those with poor OHRQoL.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".