Source of drinking water and other risk factors for dental fluorosis in Sri Lanka
Bibliographic record
Abstract
This study was done to describe the association between source of drinking water and other potential risk factors with dental fluorosis. Prevalence of dental fluorosis among 518 14-year-old students in the south of Sri Lanka was 43.2%. The drinking water sources of the students were described and fluoride samples were taken. There was a strong association between water fluoride level and prevalence of fluorosis. Tea drinking before 7 years of age was also an independent risk factor in a multivariate analysis. Having been fed with formula bottle milk as an infant seemed to increase the risk although the effect was not statistically significant. No clear effects could be found for using fluoridated toothpaste, occupation of the father, and socio-economic status. Drinking water obtained from surface water sources had lower fluoride levels (median 0.22 mg l(-1)) than water from deep tube wells (median 0.80 mg l(-1)). Most families used shallow dug wells and these had a median fluoride value of 0.48 mg l(-1) but with a wide range from 0.09 to 5.90 mg l(-1). Shallow wells located close to irrigation canals or other surface water had lower fluoride values than wells located further away. Fluoride levels have to be taken into account when planning drinking water projects. From the point of view of prevention of dental fluorosis, drinking water from surface sources or from shallow wells located close to surface water would be preferable.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".