Predictors of Plasma Fluoride Concentrations in Children and Adolescents
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it