FLUORIDATION EXPOSURE STATUS BASED ON LOCATION OF DATA COLLECTION IN THE CANADIAN HEALTH MEASURES SURVEY: IS IT VALID?
Bibliographic record
Abstract
BACKGROUND: Statistics Canada's population health surveys may be an important source of up-to-date evidence on fluoridation and population oral health. The objective of this study was to examine the validity of a geographic measure of fluoridation from a national survey (based on site of data collection), by comparing it with estimates of fluoride level from urine samples. METHODS: The data source is the environmental urine subsample (n=2563) from Cycle 2 (2009-2011) of the Canadian Health Measures Survey. Mean comparison and multivariable linear regression were used to examine whether urinary fluoride levels differed between respondents classified as "fluoridated" versus "non-fluoridated" based on data collection site. RESULTS: Respondents who attended data collection sites classified as fluoridated had significantly higher mean urinary fluoride levels than those who attended sites classified as non-fluoridated. This effect was robust to adjustment for covariates and was somewhat stronger among an "exposed" subpopulation (defined based on tap water consumption and residential history) compared with a non-exposed subpopulation. No apparent added value was associated with using a more precise geographic indicator based on home postal code. CONCLUSIONS: Fluoridation status based on data collection site seems crude, but is actually reasonably accurate compared with fluoride level in urine, in the context of a large national Canadian survey of urban and rural residents. Although findings are of limited use for individual-level risk assessment, they may be of interest to dental public health researchers and to those engaged in public health surveillance, because they inform efficient and readily available options for monitoring fluoridation status in populations.
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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 | 0.039 | 0.123 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.011 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".