Predictors of Plasma Fluoride Concentrations in Children and Adolescents
Bibliographic record
Abstract
Despite increasing concerns about neurotoxicity of fluoride in children, sources of fluoride exposure apart from municipal water fluoridation are poorly understood. We aimed to describe the associations of demographics, drinking water characteristics, diet, and oral health behaviors with plasma fluoride concentrations in U.S. children. We used data from 3928 6-19-year-olds from the 2013-2016 National Health and Nutrition Examination Survey. We used a 24-h dietary recall to estimate recent consumption of fluoridated tap water and select foods. We estimated the associations of fluoridated tap water, time of last dental visit, use of toothpaste, and frequency of daily tooth brushing with plasma fluoride concentrations. The participants who consumed fluoridated (≥0.7 mg/L) tap water (n = 560, 16%) versus those who did not had 36% (95% CI: 22, 51) higher plasma fluoride. Children who drank black or green tea (n = 503, 13%) had 42% higher plasma fluoride concentrations (95% CI: 27, 58) than non-tea drinkers. The intake of other foods and oral health behaviors were not associated with plasma fluoride concentrations. The consumption of fluoridated tap water and tea substantially increases plasma fluoride concentrations in children. Quantifying the contribution of diet and other sources of fluoride is critical to establishing safe target levels for municipal water fluoridation.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".