A Bayesian Benchmark Dose Analysis for Manganese in Drinking Water and IQ in Children Based on Pooled Data from Two Studies in Canada
Bibliographic record
Abstract
Background: Manganese (Mn) is an essential nutrient but can be neurotoxic at high exposure levels. We carried out two cross-sectional studies on the relation between water Mn levels and IQ in children. Although this metal is commonly found in drinking water, few jurisdictions have adopted a health-based guideline to limit its concentration. The Bayesian Benchmark Dose Analysis System (BBMD) can be used to estimate the level of exposure associated with a predefined health risk in a probabilistic framework.Objective: To pool samples from two studies and use BBMD to estimate the water Mn level associated with predefined levels of cognitive impairment in children, i.e. reduction of 1 and 2 IQ points.Methods: Data from two studies were pooled resulting in a sample of 630 children (ages 6-13 years). We sampled each participant's home tap water and measured Mn concentration. The performance IQ (PIQ) score was used as the primary outcome. The BBMD is based on Bayesian statistics featuring Markov Chain Monte Carlo algorithms for model fitting, parameter and quantity of interest estimation. The weight-averaged median estimate and the lower bound of the credible interval (BMDL) of multiple commonly used dose-response models are reported.Results: The concentration of Mn in drinking water associated with a decrease of 1 PIQ point was 120 μg/L (BMDL, 69 μg/L); for a decrease of 2 PIQ points, this concentration was 243 μg/L (BMDL, 140 μg/L). Different associations for water Mn and PIQ were found between sexes, so stratified analyses were also conducted. The Mn concentration associated with a decrease of 1 and 2 PIQ points in boys was 174 and 354 μg/L (BMDL, 68 and 138 μg/L) and 72 and 94 μg/L (BMDL, 9 and 19 μg/L) for girls.Conclusion: A maximum acceptable concentration for manganese in drinking water should be set to protect children, the most vulnerable population for Mn neurotoxicity. The present risk analysis can guide the decision-makers regulating water quality.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".