Exploring Dental Caries and Associated Factors in 3-Year-Old Iranian Children: An Application of Random Forest for Zero-Inflated Poisson Process
Bibliographic record
Abstract
Background: Dental caries, caused by bacterial activity leading to tooth decay, has a profound impact on children's quality of life. This study aimed to investigate factors associated with dental caries in 3-year-old Iranian children. Methods: A cross-sectional study was conducted involving 815 three-year-old children who were referred to healthcare centers in the southern region of Tehran Province, Iran. Truncated random forest, traditional random forest, and a log-linear model were employed, utilizing the number of dental caries (including excess zeros) as the outcome variable. Predictors included sex, tooth brushing, dental flossing, sweet consumption, dental visits, and parental education level. Results: The log-linear model's rate ratio (RR) indicated that boys were more likely to have at least 1 decayed tooth compared to girls (RR, 1.11). Dental floss usage significantly reduced childhood dental caries (RR, 2.74). Variable importance analysis from 2 random forests identified dental floss usage, dental visits, and the father's educational level as the most impactful factors on childhood caries. Results based on mean squared error (MSE) demonstrated that the truncated random forest (MSE, 0.002) outperformed the log-linear model (MSE, 0.959) and exhibited similar performance to the traditional random forest model (MSE, 0.006). Conclusion: The truncated random forest model demonstrated superior performance compared to traditional random forest and log-linear models. From a clinical perspective, promoting knowledge and practices related to good oral health habits in parents and their children emerges as a crucial strategy for reducing the risk of childhood caries.
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How this classification was reachedexpand
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".