Caries Risk Assessment in Children with Different Rates of Vitamin D Deficiency, Using Cariogram Model
Bibliographic record
Abstract
Objective: Vitamin D plays a very important role in improving oral and dental health as well as general health. The present study aims to evaluate the risk of caries development risk of children with and without vitamin D deficiency using the Cariogram model. Methods: This study included a total of 75 healthy children aged 6-12 years, of which 50 (35 girls and 15 boys) had different levels of vitamin D deficiency, and 25 (12 girls and 13 boys) had none. Their risk of developing new dental caries was assessed with Cariogram. SPSS v21.0 (IBM, USA) was used for analyzing the study data. In the statistical tests, a p-value less than 0.05 was considered statistically significant. Results: Mean chronological and dental ages of the participants were obtained as 9±2.32 and 8±2.36, respectively. The distribution of salivary flow rate, buffering capacities, and distributions of Lactobacilli and S. mutans counts between the groups were found to be similar. There was a significant difference between Group I and Group II and between Group I and Group III in regard to the Cariogram green percentage (percentage of chances to avoid caries), p=0.002 and p<0.001, respectively. Conclusion: In the present study, we observed a decrease in the Cariogram green sector percentage with low levels of vitamin D and an increase with normal vitamin D levels. Therefore, chances to avoid new dental caries were increased in sufficient levels of vitamin D.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".