Risk Assessment of Fluoride Intake from Tea in the Republic of Ireland and its Implications for Public Health and Water Fluoridation
Bibliographic record
Abstract
The Republic of Ireland (RoI) is the only European Country with a mandatory national legislation requiring artificial fluoridation of drinking water and has the highest per capita consumption of black tea in the world. Tea is a hyperaccumulator of fluoride and chronic fluoride intake is associated with multiple negative health outcomes. In this study, fifty four brands of the commercially available black tea bag products were purchased and the fluoride level in tea infusions tested by an ion-selective electrode method. The fluoride content in all brands tested ranged from 1.6 to 6.1 mg/L, with a mean value of 3.3 mg/L. According to our risk assessment it is evident that the general population in the RoI is at a high risk of chronic fluoride exposure and associated adverse health effects based on established reference values. We conclude that the culture of habitual tea drinking in the RoI indicates that the total cumulative dietary fluoride intake in the general population could readily exceed the levels known to cause chronic fluoride intoxication. Evidence suggests that excessive fluoride intake may be contributing to a wide range of adverse health effects. Therefore from a public health perspective, it would seem prudent and sensible that risk reduction measures be implemented to reduce the total body burden of fluoride in the population.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".