Fluoride Levels in Mexican Foods and Beverages
Bibliographic record
Abstract
INTRODUCTIONThe sources of fluoride exposure for the Mexicans are foods, beverages, fluoridated salt and naturally fluoridated water. The main objetive of this study was to estimate the concentration of fluoride in foods and beverages most frequently consumed in Mexico; in addition, their fluoride content was compared to data available from the United States (US) and the United Kingdom (UK).METHODOLOGYFrom the Health and Nutrition Survey 2012 we identified 182 foods and beverages and purchased in the biggest supermarkets chains and local markets in Mexico City. Samples were analyzed for fluoride content at least in duplicate to account for variability at the Oral Health Research Institute, Indiana University School of Dentistry, using a modification of the hexamethyldisiloxane microdiffusion method.RESULTSWe tested 166 foods and 16 beverages, classified into 14 food groups to compare with their US and UK counterparts, and finding among them a very wide range of values. Foods with the lowest and highest fluoride content were vegetable shortening (0.24μg/100g) and fried/baked pork rinds (1465.40μg/100g), respectively. The food groups with lowest and highest content were eggs (2.32μg/100g) and seafood (371.29μg/100g), respectively. When estimating the amount of fluoride ingested per portion size, the lowest values corresponded to eggs and the highest to fast food. When comparing between countries, meats and sausages, cereals, fast food, sweets and cakes, fruits, dairy products, legumes and seafood from Mexico, presented higher fluoride contents than similar foods from the US or the UK. Drinks and eggs from the US exhibited the highest fluoride contents, while this was the case for pasta, soups and vegetables from the UK.CONCLUSIONThe majority of tested Mexican foods and beverages contained higher fluoride contents than their US and UK counterparts. The Mexican data generated in this study will be useful to facilitate the monitoring of the intake 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 | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".