Three-Year Coronal Caries Incidence in Older Canadian Adults
Bibliographic record
Abstract
This paper describes the incidence of coronal caries in a sample of older adults. A 3-year follow-up study was conducted of 493 community-dwelling adults aged 50 years and over in Ontario, Canada. The incidence of coronal caries was 57.0%, and the mean net DFS increment was 1.9 surfaces. In bivariate analysis, several variables were significantly associated with incidence and/or mean DFS increment. These included: age, marital status, baseline coronal DFS, number of teeth at baseline, mean periodontal attachment loss of 4 mm or more, and wearing partial dentures. In logistic regression analysis only four factors had significant independent effects. These were level of education, marital status, mean periodontal attachment loss and number of teeth at baseline. The predictive ability of this model was fair: accuracy 65.7%, sensitivity 80.2%, and specificity 46.2%. When logistic analysis was repeated separately for two age groups, different predictors had significant independent effects, and sensitivity and specificity values differed substantially. These findings indicate predictive models for caries incidence should include both clinical and non-clinical variables because both types of variables may help to explain different aspects of coronal caries experience. Further research is required to identify other factors associated with coronal caries in older adults.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".