Quality of the water fluoridation and municipal-level indicators in a Brazilian metropolitan region
Bibliographic record
Abstract
This study explored the relationship between water fluoridation quality and development indicators at municipal level. In addition, fluoride concentrations found were classified based on two criteria for interpreting the samples. A cross-sectional ecological exploratory study was carried out including all municipalities of the metropolitan region of Great Vitória, ES, Brazil. From May to October 2016, 648 samples of water were collected covering water treatment plants responsible for more than 80% of the population of each municipality. The fluoride concentration of each sample was determined using ion-specific electrode and the results were classified according to the federal act and the criterion proposed by the Collaborating Center of the Brazilian Ministry of Health for Oral Health Surveillance. The outcome was the rate of values included in the optimal concentration interval and the independent variables were municipal-level indicators related to demographics, economics, sanitation, health conditions and human development characteristics. The Spearman test and Kappa statistic were used in the analysis. The percentage of samples presenting optimal fluoride concentration ranged from 68.1 to 81.4%, considering the two criteria used. The Kappa statistic between the criteria was 0.671 (p-value = 0.001). Human development, average coverage of supervised toothbrushing, and total population showed a strong positive correlation with the quality of fluoridation while infant mortality and tooth-extraction/dental procedures ratio showed a strong negative correlation. The plausibility of observed correlations encourages further investigations of potential causes.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".