Has the Level of Dental Fluorosis Among Toronto
Bibliographic record
Abstract
We report partial findings of a dental surveyconducted in Toronto during the 1999–2000school year. The purpose of the survey was to obtain valid estimates of the oral health status of children in the 4 regions of the recently amalgamated city. These esti-mates will be used in developing recommendations for programs to address any needs identified. The city is served by a common water system that has been fluoridated since 1963. In the fall of 1999, the concen-tration was reduced in 2 stages (from 1.2 ppm to 1.0 ppm and then to 0.8 ppm), to meet revised Canadian water standards1. A continuing oral health issue relates to the concentration of fluoride in the public water supply needed to balance the prevalence and severity of dental caries and the prevalence and severity of dental fluorosis. This paper reports recent findings on oral health status and compares fluorosis findings with those reported in earlier studies. Previous Studies of Fluorosis Interest in the prevalence and severity of dental fluorosis is reflected in the number of surveys that have been conducted. Starting with the most recent study, prevalence for children has been variously estimated for parts of the new city at the following levels: • 13.9 % for 7-year-olds in the former East York;2 • 13 % for 8- to 10-year-olds, again in the former East York;3 • 19.9 % in the former Scarborough for children 7, 9, 11 and 13 years of age;4
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".