Occlusal force predicted cognitive decline among 70- and 80-year-old Japanese: A 3-year prospective cohort study
Bibliographic record
Abstract
PURPOSE: Dementia is a growing health problem for countries with aging populations, but few effective dementia treatments are available. However, there is increasing interest in oral health as a modifiable risk factor in interventions to prevent cognitive decline. This study aimed to investigate the impact of oral health on the decline of cognitive function over 3 years among Japanese people aged 70 and 80 years. METHODS: Participants (n = 860) were community-dwelling older adults who participated in baseline and follow-up surveys (at baseline: 69-71 years n = 423; 79-81 years, n = 437). Registered dentists examined the number of teeth, number of functional teeth, number of periodontal teeth, and occlusal force. The Japanese version of the Montreal Cognitive Assessment was used to evaluate cognitive function. We also evaluated socioeconomic factors, medical history, drinking and smoking habits, physical performance, genetic factors, and C-reactive protein concentration in blood. A generalized estimating equation (GEE) was used to examine how oral health at baseline influenced cognitive decline over 3 years. RESULTS: The GEE showed that the number of teeth (non-standardized coefficient: B = 0.031, p = 0.022) and occlusal force (B = 0.103, p = 0.004) at baseline were associated with cognitive function at follow-up, even after adjusting for other risk factors. Furthermore, maintaining more teeth (B = 0.009, p = 0.004) and a stronger occlusal force (B = 0.020, p = 0.040) buffered cognitive decline. CONCLUSIONS: Number of teeth and occlusal force predict cognitive decline over 3 subsequent years in Japanese older adults aged 70 and 80 years.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 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".