Low Tongue Strength and the Number of Teeth Present Are Associated with Cognitive Decline in Older Japanese Dental Outpatients: A Cross-Sectional Study
Bibliographic record
Abstract
To mitigate the impact of dementia, initiating early intervention is important. This study aims to investigate the associations between deterioration in oral function and cognitive decline in older outpatients whose oral health was maintained in the dental clinic. This study included 50 outpatients aged ≥65 years. We used the Japanese version of the Montreal Cognitive Assessment (MoCA-J) to assess cognitive decline. Oral function was evaluated by tongue pressure, masticatory performance, and swallowing ability. A full-mouth periodontal examination was conducted, and the occlusal support and number of teeth were recorded. Odds ratios (ORs) and 95% confidence intervals (CIs) for cognitive decline (MoCA-J score ≤25 points) were calculated using logistic regression models. The age, number of teeth, tongue pressure, and masticatory performance were significantly correlated with cognitive decline (p < 0.05). Logistic regression analyses revealed that cognitive decline was independently associated with age (OR: 1.25; 95% CI: 1.03–1.52; p = 0.024), number of teeth (OR = 0.83; 95% CI: 0.76–1.00; p = 0.047), and lower tongue pressure (OR: 0.87; 95% CI: 0.77–0.98; p = 0.022). Lower tongue pressure and a small number of remaining teeth may be associated with cognitive decline in Japanese outpatients.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".