Stress, Depression, Cortisol, and Periodontal Disease
Bibliographic record
Abstract
BACKGROUND: Stress and depression may affect the onset and progression of periodontal disease. However, to the best of our knowledge, no published study has established whether the mechanisms by which stress and depression influence periodontal disease are physiologic, behavioral, or both. This cross-sectional pilot study explored the associations between psychologic factors, markers of periodontal disease, psychoneuroimmunologic variables, and behavior. METHODS: This study included 45 periodontal patients referred by three dentists. Participants completed composite health, chronic stress, depression, and demographic questions, and salivary cortisol (CORT) was measured. A hygienist assessed the magnitude of periodontal disease. RESULTS: Stress, depression, and CORT were correlated with measures of periodontal disease. In addition, oral care neglect during periods of stress and depression was associated with attachment loss and missing teeth. After controlling for age, family history, and brushing frequency, depression and CORT were significant predictors of the number of missing teeth. A similar model also predicted the number of teeth with clinical attachment loss >5 mm. CONCLUSIONS: Stress and depression may be associated with periodontal destruction through behavioral and physiologic mechanisms. Addressing psychologic factors, such as depression, may be an important part of periodontal preventive maintenance.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".