Maternal fluoride exposure, fertility and birth outcomes: The MIREC cohort
Bibliographic record
Abstract
Fluoride exposure >1.5 mg/L from water has been associated with adverse pregnancy and birth outcomes. Little is known, however, about the effect of fluoride at levels consistent with water fluoridation (i.e., 0.7 mg/L) on pregnancy and birth outcomes. We examined the relationship between maternal fluoride exposure, fertility, and birth outcomes in a Canadian pregnancy cohort living in areas where municipal drinking water fluoride concentrations ranged from 0.04 to 0.87 mg/L. Using data from the Maternal-Infant Research on Environmental Chemicals (MIREC) study, we estimated fluoride exposure during pregnancy using three different metrics: (1) maternal urinary fluoride concentrations standardized for specific gravity (MUFSG) and averaged across all three trimesters (N = 1566), (2) water fluoride concentration (N = 1370), and (3) fluoride intake based on self-reported consumption of water, coffee, and tea, adjusted for body weight (N = 1192). Data on fertility, birth weight, gestational age, preterm birth, and small-for-gestational age (SGA) were assessed. We used multiple linear regression to examine associations between fluoride exposure, birth weight and gestational age, and logistic regression to examine associations with fertility, preterm birth, and SGA, adjusted for relevant covariates. Median (IQR) MUFSG was 0.50 (0.33–0.76) mg/L, median water fluoride was 0.52 (0.17–0.64) mg/L, and median fluoride intake was 0.008 (0.003–0.013) mg/kg/day. MUFSG, water fluoride concentrations, and fluoride intake were not significantly associated with fertility, birth weight, gestational age, preterm birth, or SGA. Fetal sex did not modify any of the associations. Fluoride exposure during pregnancy was not associated with fertility or birth outcomes in this Canadian cohort.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".