Dietary Fluoride Intake and Associated Skeletal and Dental Fluorosis in School Age Children in Rural Ethiopian Rift Valley
Bibliographic record
Abstract
An observational study was conducted to determine dietary fluoride intake, diet, and prevalence of dental and skeletal fluorosis of school age children in three fluorosis endemic districts of the Ethiopian Rift Valley having similar concentrations of fluoride (F) in drinking water (~5 mg F/L). The duplicate plate method was used to collect foods consumed by children over 24 h from 20 households in each community (n = 60) and the foods, along with water and beverages, were analyzed for fluoride (F) content. Prevalence of dental and skeletal fluorosis was determined using presence of clinical symptoms in children (n = 220). Daily dietary fluoride intake was at or above tolerable upper intake level (UL) of 10 mg F/day and the dietary sources (water, prepared food and beverages) all contributed to the daily fluoride burden. Urinary fluoride in children from Fentale and Adamitulu was almost twice (>5 mg/L) the concentration found in urine from children from Alaba, where rain water harvesting was most common. Severe and moderate dental fluorosis was found in Alaba and Adamitulu, the highest severity and prevalence being in the latter district where staple foods were lowest in calcium. Children in all three areas showed evidence of both skeletal and non-skeletal fluorosis. Our data support the hypothesis that intake of calcium rich foods in addition to using rain water for household consumption and preparation of food, may help in reducing risk of fluorosis in Ethiopia, but prospective studies are needed.
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.
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 | 0.001 | 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) | 0.001 | 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".