Periodontitis and perceived risk for periodontitis in elders with evidence of depression
Bibliographic record
Abstract
BACKGROUND: Depression and periodontitis are common conditions in older adults. There is some evidence that these two conditions may be related. AIMS: To study a population of dentate elders and assess the prevalence of depression, self-assessment of risk for periodontitis and tooth loss, in relation to periodontal disease status. MATERIAL AND METHODS: Data were obtained from 701 older subjects (mean age 67.2 years (SD+/-4.6), of whom 59.5% were women. Self-reports of a diagnosis of depression, scores of the Geriatric Depression Scale (GDS), and self-assessment of risk for future tooth loss and periodontitis were compared with a diagnosis of periodontitis based on probing depth, and bone loss assessed from panoramic radiographs. Other systemic diseases and smoking habits were also determined and studied in relation to depression. RESULTS: A history of depression was reported by 20% of the subjects. GDS scores >/=8 were reported by 9.8% of the elders. Periodontitis was identified in 48.5% of the subjects. Depression was associated with heart attack (p<0.05), stroke (p<0.01), high blood pressure (p<0.02), all combined cardiovascular diseases (p<0.001), chronic pain (p<0.01), osteoarthritis (p<0.001), and osteoporosis (p< 0.001) but not with periodontitis (p=0.73). Subjects with depression had a higher self-reported risk score for future tooth loss (p<0.02). No group difference emerged for self-perceived risk for periodontitis. Logistic regression analysis demonstrated that a past history of tooth loss (p<0.001), self-perceived risk for periodontitis (p<0.02), the number of years with a smoking habit (p<0.02), and male gender (p<0.02) were associated with a diagnosis of periodontitis but neither measure of depression could be included in an explanatory model for periodontitis. CONCLUSIONS: Evidence of depression (self-report or by GDS) is not associated with risk for periodontitis in older subjects but is associated with tooth loss and chronic conditions associated with pain.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".