Socio-Demographic Features and Fluoride Technologies Contributing to Higher Fluorosis Scores in Permanent Teeth of Canadian Children
Bibliographic record
Abstract
OBJECTIVE: To examine levels of fluorosis among children in two Canadian communities exposed to fluoride. BACKGROUND: One community had discontinued fluoride, the other had maintained it. Water supplies, however, were fluoridated for all the children when their esthetically important teeth were mineralized. METHODS: We examined 8,277 children to assess Thystrup-Fejerskov Index (TFI) scores. Multivariate Poisson regression models were used to identify the relationship between TFI and water fluoride status, age, gender, SES, and dietary and fluoride exposure histories (supplements, rinses, toothpaste amount, tooth brushing frequency, and tooth brushing starting age). Parent(s) completed questionnaires. RESULTS: Overall, levels of fluorosis were low to mild, with residents of the fluoridation-ended communities having marginally higher TFI scores than those of the still-fluoridated community. Females had higher TFI scores than males. Children aged 10 years or more had higher TFI scores than younger children. Consuming bottled water between birth and 6 months of age was protective. Exposure to fluoridation technologies was consistently associated with fluorosis experience. Children who began brushing with fluoride toothpaste between their first and second birthdays had higher TFI scores than those who began between their second and third birthdays, regardless of daily brushing frequency. Children who regularly used supplements had higher TFI scores than those who did not. Children with a college-educated father had higher TFI scores than those whose fathers had less education. CONCLUSIONS: Higher fluoride exposure slightly increased the likelihood that a child had a higher TFI score, especially when more fluoridation technologies were used at home.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".