Three-Year Coronal Caries Incidence in Older Canadian Adults
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 it