Determining the prevalence and risk factors for early childhood caries in a community dental health clinic.
Bibliographic record
Abstract
PURPOSE: The purposes of this study were to: (1) determine the prevalence of early childhood caries (ECC) among young children accessing dental services at a community dental clinic; (2) identify factors associated with the presence of ECC; and (3) determine the percentage of children who received treatment for ECC in this setting and the number who required referral to specialists. METHODS: The study population comprised children younger than 72 months attending the clinic between 1991 and 2004. A chart review was conducted. RESULTS: Eight hundred thirty-four charts met inclusion criteria; 71% had ECC, while the mean deft was 3.7+/-3.9 (SD). The average age at the first visit was 50.0+/-12.7 (SD) months. Those with ECC were significantly older at the first visit (P<.001), and the prevalence increased with family size (P=.011) and number of siblings (P=.019). ECC children were significantly more likely to come from households with lower monthly incomes (P=.033). The prevalence of ECC did not vary according to specific areas in Winnipeg where children resided (P=.20). CONCLUSIONS: Key risk factors for ECC included: (1) the child's sex; (2) low monthly income; (3) whether the child resided with both parents; and (4) a history of foiled dental visits. These data may assist in identifying children at greatest risk for ECC and may help public health agencies develop appropriate prevention strategies, including promoting early dental visits for infants.
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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 | 0.004 |
| 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 | 0.001 | 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".