Mean Fluoride Concentration in Drinking Water Sources of a Municipality: A Descriptive Cross-sectional Study
Bibliographic record
Abstract
Introduction: Fluoride is essential for the growth and development of teeth and bone. Excess or less fluoride consumption can have harmful effects on our bodies. Fluoride level of 0.5-1.5 mg/l is said to be optimized according to the World Health Organization. The level of fluoride varies among the different geographical regions and water sources. It is essential to find out the fluoride content of various water sources used for drinking purposes. The aim of this study was to find the mean concentration of fluoride in drinking water sources of a municipality. Methods: A descriptive cross-sectional study was conducted in a Municipality. The study was conducted from 1 December 2021 to 30 December 2021 after receiving ethical approval from the Ethical Review Board (Reference number: 1134). Water samples were collected and the fluoride content was estimated using 2-parasulfophenylazo-1,8-dihydroxy-3,6-napthalene-disulfonate colorimetric method. The data collected from the laboratory were calculated and presented in the form of a table. Point estimate and 95% Confidence Interval were calculated. Results: The mean value of fluoride content in 160 collected water samples was 0.369±0.275 mg/l (0.33-0.41, 95% Confidence Interval). Among the different wards, the fluoride content was 0.708±0.27 mg/l in ward number 12 followed by a fluoride content of 0.57±0.19 mg/l in ward number 5. Conclusions: In this study, the mean fluoride levels were lower when compared with similar studies conducted in similar settings. The levels were lower than that recommended by the World Health Organization. The various controlled methods of fluoridation have to be quickly initiated. Other means of fluoride consumption, like the use of fluoridated toothpaste, has to be recommended. Keywords: dental caries; drinking water; fluoride.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".