Hubungan Stunting dengan Tingkat Keparahan Karies Gigi pada Anak Usia 10-12 Tahun di Kecamatan Tuah Negeri Kabupaten Musi Rawas
Bibliographic record
Abstract
Stunting is one of the most common malnutrition conditions. Stunting conditions can cause impaired child development including developmental disorders in the oral cavity. Stunting children are more susceptible to dental caries due to changes in saliva characteristics. This study aims to analyze the relationship between stunting and the severity of dental caries in elementary school-aged children in Tuah Negeri District, Musi Rawas Regency. Methods: This study is an analytic observational study with a cross-sectional design. A sample of 70 people was taken randomly from elementary school students in Tuah Negeri District, Musi Rawas Regency. Determination of nutritional status based on anthropometric measurements (TB/U). DMFT examination was carried out by looking at decaying, filling, and missing teeth, then the severity of dental caries was categorized into low (DMFT 0 - 2.6), moderate (DMFT 2.7 - 4.4), high (DMFT > 4.5). Data were analyzed using SPSS version 20 with Chi-Square Test analysis. Result: The results of the measurement of nutritional status showed that 34 children (48.6%) were stunted. In stunting children, there are 15 children (44.12%) in the low category, 16 children (47.06%) in the medium category, 3 children (8.82%) in the high category. The results of the bivariate analysis obtained p = 0.000. Conclussion: There is a significant relationship between stunting and the severity of dental caries in children aged 10-12 years in Tuah Negeri District, Musi Rawas Regency. It is necessary to increase efforts to promote health related to stunting and dental health through health education activities by involving the role of parents in choosing food intake and in maintaining children's dental and oral hygiene
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.011 | 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 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".