The life course approach: explaining the association between height and dental caries in Brazilian adolescents
Bibliographic record
Abstract
AIM: To investigate the relationship between height and dental caries in Brazilian adolescents. METHODS: A cross-sectional survey design was used to collect retrospective data. Of 764 eligible 13-year-old adolescents enrolled in urban private or public schools in a Brazilian town, 652 were clinically examined and interviewed. Data were collected on socioeconomic circumstances, family related variables, oral health behaviour and anthropometric measures (height and weight). Dental caries was measured by decayed, missing and filled teeth (DMFT) index. The DMFT was categorized according to two levels of severity (low DMFT </= 6; high DMFT > 6) using the 75th percentile of the distribution as the cut-off point. Data analysis involved multiple logistic regression. RESULTS: Adolescents who were the second or later child were 1.90 times more likely to have a high DMFT, whilst being a taller adolescent had a protective effect on caries experience (OR = 0.04; 95% CI = 0.00-0.79). In addition, adolescents from rural areas (OR = 2.74; 95% CI = 1.56-4.82), those whose mothers had less than 8 years of education (OR = 2.10; 95% CI = 1.03-4.27) and those who reported high levels of paternal punishment (OR = 1.60; 95% CI = 1.02-2.52) had an increased risk of having a high DMFT. CONCLUSION: There is a relationship between height and dental caries experience in this sample of Brazilian adolescents.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".