The Association Between Body Mass Index and Dental Caries: Cross-Sectional study
Bibliographic record
Abstract
BACKGROUND: Obesity is a growing health-related problem worldwide. Both obesity and dental caries are important health issues with multifactorial aspects. Some studies have shown an association between body mass index (BMI) and caries in childhood/adolescence but limited data about such an association are available in adults. The primary goal of this study was to assess the prevalence of dental caries and its relationship to BMI. METHODS: We conducted a cross-sectional study at Taif University Outpatient Clinic, for adults who had a visit to the dental clinic. Baseline characteristics were obtained by the participating physician. The decayed, missing, and filled teeth (DMFT) index was used to determine the prevalence of dental caries. Information about healthy eating, smoking, exercise, sleep patterns, media consumption, and brushing habits were collected. RESULTS: A total of 385 patients were enrolled with a mean age of 28.39 years, 72.8% were male, mean DMFT index score was 6.55, and 85.5% reported brushing their teeth at least once daily. Of the participants, 55.3% were either overweight or obese, and 42.2% demonstrated a high prevalence of dental caries with no significant difference in BMI when compared to the low dental caries group. CONCLUSIONS: A high prevalence of overweight/obesity and dental caries was observed among the participants. After controlling for potential confounders like smoking and brushing habits, significant positive correlation between BMI and DMFT was observed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".