Relationship of Salivary Occult Blood With General and Oral Health Status in Employees of a Japanese Department Store
Bibliographic record
Abstract
BACKGROUND: The Japanese Ministry of Health started screening for periodontal disease as part of senior health services in 1995. However, only a small number of workplaces conduct regular dental checkups in Japan. Therefore, the prevalence of periodontal disease and other oral health conditions has not been fully determined in workers in Japan. This study aimed to clarify the usefulness of a salivary occult blood test (SOBT) to assess periodontal disease, and to investigate the association of salivary occult blood with general and oral health in Japanese employees. METHODS: A cross-sectional study was conducted among department store workers in Hiroshima city. Subjects were 449 workers who received regular health checkups including dental examinations in 2018. An SOBT using monoclonal antibody against human hemoglobin was performed. Periodontal status was evaluated using the Community Periodontal Index (CPI). We investigated the association of salivary occult blood with general and oral health in 338 employees (85 men, 253 women; mean age 41.4 years, range 19 - 69 years). RESULTS: Univariate analysis revealed a significant relationship between salivary occult blood and sex, body mass index (BMI), diabetes, CPI, daily brushing frequency, and number of untreated teeth (P = 0.034, P = 0.003, P = 0.022, P = 0.007, P = 0.004, and P = 0.015, respectively). Furthermore, BMI, diabetes, CPI, and brushing frequency were significantly associated with salivary occult blood in binomial logistic regression analysis (odds ratio 1.09, P = 0.014; odds ratio 9.38, P = 0.047; odds ratio 1.31, P = 0.004; and odds ratio 0.70, P = 0.045, respectively). These results suggest that positivity in the SOBT is importantly associated with periodontal disease and diabetes. Interestingly, subjects aged ≥ 35 years with metabolic syndrome exhibited a significantly higher positive rate of salivary occult blood than those without metabolic syndrome (P = 0.01). CONCLUSIONS: The SOBT was a reliable screening method for periodontal disease, and positivity in the test was related to diabetes in Japanese employees. Further examinations are required to clarify the association of salivary occult blood with other systemic diseases.
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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".