Prevalence of dental caries among 7- and 13-year-old First Nations children, District of Manitoulin, Ontario.
Bibliographic record
Abstract
PURPOSE: Dental caries is a disease that, although decreasing in the non-Aboriginal child population, remains high for Canadian Aboriginal and Native American children and adolescents. To address dental health issues in First Nations in the District of Manitoulin, Noojmowin Teg Health Centre initiated a multiphase collaborative research project with the department of community dentistry at the University of Toronto. The purpose of this paper was to identify the prevalence of dental caries in children 7 or 13 years of age and to compare these data with published data for the same age groups from other First Nations communities in Canada. METHODS: All children 7 or 13 years of age who were in elementary schools on a reserve in 7 First Nations communities were eligible for a dental health examination as part of the survey. Children attending school off the reserve in 6 of the communities were also eligible. RESULTS: A total of 66 children (56% 7-year-old children, 62% girls) were examined. The mean caries score (deft+ DMFT) for 7-year-old children was 6.2; the mean decayed, extracted, filled permanent teeth (DMFT) score for 13-year-old children was 4.1. Overall, 96% of children had 1 or more past or active carious lesion. CONCLUSION: Results indicate that dental caries is highly prevalent and increasing in severity in this population.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".