Dental Pocket and Type 2 Diabetes among Elderly People Aged 88 in Japan
Bibliographic record
Abstract
BACKGROUND: This study aimed to clarify the relationship between periodontal pocket depth and type 2 diabetes in individuals aged 88. We examined the relationship between periodontal pockets and type 2 diabetes in 590 older adults aged 88 years in Japan. METHOD: The subjects of this study were 664 individuals who underwent a dental check-ups in Matsudo city for individuals aged 88 years. The periodontal pocket recorded by trained dentists was categorized as healthy and mildly equated pocket <5 mm. We performed univariate and multivariate binomial logistic regression analyses to examine the association of the type of dental pocket and type 2 diabetes. Unadjusted and covariate-adjusted odds ratios (ORs) and 95％confidence intervals (CIs) were calculated for the type 2 diabetes. RESULTS: Valid responses without missing data from 590 respondents were used in the analyses. The multivariate analysis indicated a significant association between deep periodontal pockets and type 2 diabetes (OR: 2.02, 95%CI: 1.13-3.59). CONCLUSION: This survey indicated the possibility that the prevalence of type 2 diabetes was high among older adults aged 88 years with deep periodontal pockets. A synergistic improvement effect can be expected from the health management for older adults in later stages, which includes glycemic control and oral health management. The study recommends that proper dental health check-up and maintenance of good oral health are important for preventing type 2 diabetes even in individuals aged 88.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 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".