Risk Indicators for Periodontal Disease in a Remote Canadian Community—a Dental Practice‐based Study
Bibliographic record
Abstract
OBJECTIVES: The purpose of this cross-sectional study was to identify risk markers and risk indicators for periodontal attachment loss in a remote Canadian community. Of special interest was the association between smoking and periodontal disease experience. METHODS: Data were collected from a convenience sample of 187 adult patients attending a dental office in a rural community located in Northern Ontario. Information was obtained via a questionnaire and a periodontal examination. The questionnaire included the use of dental services, self-care behaviors, general health status, smoking, and personal characteristics. Periodontal health was assessed using the mean periodontal attachment loss (MPAL), measured at two sites on all remaining teeth and the proportions of sites examined with loss of 2 mm or more and 5 mm or more. Plaque scores and measures of the number of missing teeth also were obtained. The relationships between mean periodontal attachment loss, the proportion of sites with 5 mm or more of loss and independent variables such as age, sex, current smoking status, mean tooth plaque scores, flossing frequency, and regularity of preventive dental visits were examined in bivariate and multivariate analyses. RESULTS: The data revealed a mean periodontal attachment loss of 3.9 mm (SD=1.5). The mean proportion of sites examined with loss of 2 mm or more was 0.89 and the mean proportion with loss of 5 mm or more was 0.35. In linear regression analysis, plaque scores, the number of missing teeth, age, current smoking status, regularity of dental visits, and flossing frequency had statistically significant independent effects and explained 60.0 percent of the variance in mean periodontal attachment loss. Just over 30 percent of subjects had severe periodontal disease, defined as 50 percent or more of sites examined with loss of 5 mm or more. In logistic regression analysis, missing teeth, dental visiting, smoking status, age, and flossing frequency had significant independent effects. The strongest association observed was with smoking, which had an odds ratio of 6.3. The logistic regression model correctly predicted 64.3 percent of cases with severe disease. CONCLUSIONS: The data indicate that the periodontal health of these patients is poor. Risk indicators or markers of poor periodontal health in the population studied included missing teeth, plaque scores, age, current smoking status, regularity of dental visits, and flossing frequency. This supports previous findings that behavioral factors play an important role in periodontal disease.
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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 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".