Oral Hygiene Status in 88-Year-Olds with a History of Myocardial Infarction and Stroke
Bibliographic record
Abstract
BACKGROUND: This cross-sectional study aimed to examine the relationship between oral hygiene and a history of myocardial infarction or stroke in elderly individuals. METHOD: The study was conducted in Matsudo City, Chiba Prefecture, and included 664 individuals aged 88 who underwent dental check-ups between 2019 and 2021. Data on oral health, demographics, and medical history, including infarction and stroke, were collected through dental check-ups and questionnaires administered by dentists. RESULTS: Results showed that 24.5% of participants had poor oral hygiene, while 75.5% had good oral hygiene. A higher incidence of poor oral hygiene was found in those with a history of myocardial infarction or stroke, with a 1.6-fold increase compared to those without such a history. Multivariate logistic regression analysis revealed that females were significantly less likely to have poor oral hygiene (OR: 0.50, 95% CI: 0.32–0.77), whereas individuals with a history of infarction were more likely to have poor oral hygiene (OR: 1.63, 95% CI: 1.03–2.57). CONCLUSION: The study highlights the importance of oral hygiene management in elderly individuals, particularly those with a history of cardiovascular events, as poor oral hygiene is linked to systemic conditions such as aspiration pneumonia. These findings support the promotion of dental check-up programs for the elderly, as part of broader efforts to enhance quality of life and prevent systemic diseases in aging populations.
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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 | 0.000 |
| 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".