Association between Periodontal Disease and Peptic Ulcers among Japanese Workers: MY Health Up Study
Bibliographic record
Abstract
OBJECTIVE: This study aimed to investigate the association between periodontal disease and peptic ulcers in a working population. METHODS: Self-administered questionnaires were distributed to all employees of a large insurance company in Japan. The questionnaire asked about their health status and lifestyle habits. Peptic ulcer was defined as either stomach ulcer, duodenal ulcer, or both. For the evaluation of periodontal disease, three indices were used: (a) loss of five or more teeth, (b) having been told of having periodontitis, and (c) periodontal risk score. RESULTS: Of the eligible 28 765 subjects analyzed, peptic ulcer was present in 397 (1.4%). The results of bivariate analyses showed that a significantly higher proportion of subjects with peptic ulcer reported that they lost five or more teeth (35.3 vs. 17.4%, p<0.001) or that they were told they had periodontitis (33.5 vs. 20.7%, p<0.001). Moreover, the periodontal risk score was higher for those with peptic ulcer than those without (mean 0.83 vs. 0.59, p<0.001). In multivariate logistic regression analyses, statistical associations were found between the presence of peptic ulcer and loss of five or more teeth (odds ratio (OR): 1.41, 95% confidence interval (CI): 1.13-1.76, p<0.01), having been told of having periodontitis (OR: 1.28, 95% CI: 1.03-1.59, p<0.05), and a 1-point increase in the periodontal risk score (OR: 1.17, 95% CI: 1.04-1.30, p<0.01), respectively. CONCLUSION: Modest but statistically significant associations were found between the self-reported measures of periodontal disease and peptic ulcers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".