Racial/ethnic inequality in the association of allostatic load and dental caries in children
Bibliographic record
Abstract
OBJECTIVES: Allostatic load (AL), defined as the overtime "wear and tear" on biological systems due to stress, disproportionately affects racial/ethnic minorities and has been shown to associate with racial inequality in oral health in the adult population. This study aims to assess racial/ethnic inequality in AL and untreated dental caries (UD) in children, and to assess the association between allostatic load and UD, and whether it varies by race/ethnicity. METHODS: Data from the National Health and Nutrition Examination Survey (NHANES) (2001-2010) for 8-17-year-old children (n = 11,378) was used. AL scores were generated using cardiovascular, metabolic and immune biomarkers. Multivariable log binomial regression models adjusted for age, sex, poverty: income ratio (PIR), health insurance status and the frequency of healthcare visits, were used to assess the relationships of interest. RESULTS: Racial/ethnic inequality was evident in UD and AL, where Mexican American and black children exhibited more UD and a higher AL score than white. AL was associated with UD in fully adjusted models. This association was significant across all racial/ethnic groups, but was stronger in Mexican American and black children, compared to their white counterparts. CONCLUSIONS: Similar racial inequality is evident in AL and UD that is not explained by poverty and/or behavioral factors. Racial/ethnic inequality is also evident in the association between AL and UD.
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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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".