The Relationship Between Oral Health and Cognitive Function Among Community‐Dwelling Japanese Older Adults: A Cross-sectional Study Using Toon Health Study Data
Bibliographic record
Abstract
Abstract Background This study aimed to investigate the relationship between tooth loss and cognitive function in community-dwelling elderly. Methods A total of 438 men and 715 women aged 60–84 years who participated in the Toon Study—an epidemiological study conducted among local residents of Toon City, Ehime Prefecture, Japan from 2014 to 2018—were included. A self-administered questionnaire was used to assess oral health status (number and bite of teeth). Mild cognitive impairment (MCI) was assessed using the Japanese version of the Montreal Cognitive Assessment, with scores < 26 considered as MCI. The odds ratios (OR) and 95% confidence intervals (95% CI) of MCI were compared with having 25 + teeth and good masticatory status, after adjusting for age, sex, and other potential confounding factors using a logistic regression model. Results The multivariate adjusted ORs (95% CIs) of MCI for having < 15 teeth compared with ≥ 25 was 1.34 (0.97–1.84). Additionally, that of poor masticatory performance compared with good masticatory status was 1.41 (1.06–1.88). Associations were evident in those aged < 75, with ORs (95% CIs) for number of teeth and masticatory status of 1.58 (1.10–2.27) and 1.50 (1.09–2.08), respectively. This was in contrast to those aged ≥ 75 years. Conclusions Our findings suggest that, to maintain cognitive function, maintaining both the number of teeth and the complex oral function of bite in individuals aged < 75 years is vital.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".