Dental Pocket and Type 2 Diabetes among Elderly People Aged 88 in Japan - Report on Improvements to Analytical Methods
Bibliographic record
Abstract
BACKGROUND: This study aimed to clarify the relationship between periodontal pocket depth and type 2 diabetes in individuals aged 88. We re-examined the relationship between periodontal pockets and type 2 diabetes in 508 older adults aged 88 years, excluding those individuals without residual teeth or teeth available for periodontal pocket measurement. METHODS: The subjects of this study were individuals who underwent dental check-ups in Matsudo city for individuals aged 88 years. We performed binomial logistic regression analyses to examine the association of presence of periodontal pockets and type 2 diabetes. RESULTS: Logistic regression analyses with covariates showed a significant association between deep periodontal pockets and type 2 diabetes in the continuous teeth model (OR: 2.26, 95% CI: 1.23–4.16) and in the teeth categorical model (OR: 2.04, 95% CI: 1.22–3.44). The results were almost identical to the original findings. CONCLUSION: A significant association was identified between periodontal pockets and an increased prevalence of type 2 diabetes among 508 Japanese individuals aged 88 years, after excluding those without residual teeth or teeth eligible for periodontal pocket measurement. These findings closely align with the original results and reinforce the importance of promoting dental check-ups, highlighting the role of oral hygiene in preventive healthcare even for those 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 | 0.001 | 0.003 |
| 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 | 0.001 |
| 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".